{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# TensorFlow Classification"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data\n",
    "\n",
    "https://archive.ics.uci.edu/ml/datasets/pima+indians+diabetes\n",
    "https://www.kaggle.com/uciml/pima-indians-diabetes-database\n",
    "\n",
    "1. Title: Pima Indians Diabetes Database\n",
    "\n",
    "2. Sources:\n",
    "   (a) Original owners: National Institute of Diabetes and Digestive and\n",
    "                        Kidney Diseases\n",
    "   (b) Donor of database: Vincent Sigillito (vgs@aplcen.apl.jhu.edu)\n",
    "                          Research Center, RMI Group Leader\n",
    "                          Applied Physics Laboratory\n",
    "                          The Johns Hopkins University\n",
    "                          Johns Hopkins Road\n",
    "                          Laurel, MD 20707\n",
    "                          (301) 953-6231\n",
    "   (c) Date received: 9 May 1990\n",
    "\n",
    "3. Past Usage:\n",
    "    1. Smith,~J.~W., Everhart,~J.~E., Dickson,~W.~C., Knowler,~W.~C., \\&\n",
    "       Johannes,~R.~S. (1988). Using the ADAP learning algorithm to forecast\n",
    "       the onset of diabetes mellitus.  In {\\it Proceedings of the Symposium\n",
    "       on Computer Applications and Medical Care} (pp. 261--265).  IEEE\n",
    "       Computer Society Press.\n",
    "\n",
    "       The diagnostic, binary-valued variable investigated is whether the\n",
    "       patient shows signs of diabetes according to World Health Organization\n",
    "       criteria (i.e., if the 2 hour post-load plasma glucose was at least \n",
    "       200 mg/dl at any survey  examination or if found during routine medical\n",
    "       care).   The population lives near Phoenix, Arizona, USA.\n",
    "\n",
    "       Results: Their ADAP algorithm makes a real-valued prediction between\n",
    "       0 and 1.  This was transformed into a binary decision using a cutoff of \n",
    "       0.448.  Using 576 training instances, the sensitivity and specificity\n",
    "       of their algorithm was 76% on the remaining 192 instances.\n",
    "\n",
    "4. Relevant Information:\n",
    "      Several constraints were placed on the selection of these instances from\n",
    "      a larger database.  In particular, all patients here are females at\n",
    "      least 21 years old of Pima Indian heritage.  ADAP is an adaptive learning\n",
    "      routine that generates and executes digital analogs of perceptron-like\n",
    "      devices.  It is a unique algorithm; see the paper for details.\n",
    "\n",
    "5. Number of Instances: 768\n",
    "\n",
    "6. Number of Attributes: 8 plus class \n",
    "\n",
    "    7. For Each Attribute: (all numeric-valued)\n",
    "       1. Number of times pregnant\n",
    "       2. Plasma glucose concentration a 2 hours in an oral glucose tolerance test\n",
    "       3. Diastolic blood pressure (mm Hg)\n",
    "       4. Triceps skin fold thickness (mm)\n",
    "       5. 2-Hour serum insulin (mu U/ml)\n",
    "       6. Body mass index (weight in kg/(height in m)^2)\n",
    "       7. Diabetes pedigree function\n",
    "       8. Age (years)\n",
    "       9. Class variable (0 or 1)\n",
    "\n",
    "8. Missing Attribute Values: Yes\n",
    "\n",
    "9. Class Distribution: (class value 1 is interpreted as \"tested positive for\n",
    "   diabetes\")\n",
    "\n",
    "   Class Value  Number of instances\n",
    "   0            500\n",
    "   1            268\n",
    "\n",
    "10. Brief statistical analysis:\n",
    "\n",
    "        Attribute number:    Mean:   Standard Deviation:\n",
    "        1.                     3.8     3.4\n",
    "        2.                   120.9    32.0\n",
    "        3.                    69.1    19.4\n",
    "        4.                    20.5    16.0\n",
    "        5.                    79.8   115.2\n",
    "        6.                    32.0     7.9\n",
    "        7.                     0.5     0.3\n",
    "        8.                    33.2    11.8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "diabetes = pd.read_csv('pima-indians-diabetes.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Number_pregnant</th>\n",
       "      <th>Glucose_concentration</th>\n",
       "      <th>Blood_pressure</th>\n",
       "      <th>Triceps</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>Pedigree</th>\n",
       "      <th>Age</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>0.743719</td>\n",
       "      <td>0.590164</td>\n",
       "      <td>0.353535</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.500745</td>\n",
       "      <td>0.234415</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.427136</td>\n",
       "      <td>0.540984</td>\n",
       "      <td>0.292929</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.396423</td>\n",
       "      <td>0.116567</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>0.919598</td>\n",
       "      <td>0.524590</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.347243</td>\n",
       "      <td>0.253629</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0.447236</td>\n",
       "      <td>0.540984</td>\n",
       "      <td>0.232323</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.418778</td>\n",
       "      <td>0.038002</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0.688442</td>\n",
       "      <td>0.327869</td>\n",
       "      <td>0.353535</td>\n",
       "      <td>0.198582</td>\n",
       "      <td>0.642325</td>\n",
       "      <td>0.943638</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Number_pregnant  Glucose_concentration  Blood_pressure   Triceps   Insulin  \\\n",
       "0                6               0.743719        0.590164  0.353535  0.000000   \n",
       "1                1               0.427136        0.540984  0.292929  0.000000   \n",
       "2                8               0.919598        0.524590  0.000000  0.000000   \n",
       "3                1               0.447236        0.540984  0.232323  0.111111   \n",
       "4                0               0.688442        0.327869  0.353535  0.198582   \n",
       "\n",
       "        BMI  Pedigree  Age  Class  \n",
       "0  0.500745  0.234415   50      1  \n",
       "1  0.396423  0.116567   31      0  \n",
       "2  0.347243  0.253629   32      1  \n",
       "3  0.418778  0.038002   21      0  \n",
       "4  0.642325  0.943638   33      1  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diabetes.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Number_pregnant', 'Glucose_concentration', 'Blood_pressure', 'Triceps',\n",
       "       'Insulin', 'BMI', 'Pedigree', 'Age', 'Class'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diabetes.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Clean the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "cols_to_norm = ['Number_pregnant', 'Glucose_concentration', 'Blood_pressure', 'Triceps',\n",
    "       'Insulin', 'BMI', 'Pedigree']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Normalize**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "diabetes[cols_to_norm] = diabetes[cols_to_norm].apply(lambda x: (x - x.min()) / (x.max() - x.min()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Number_pregnant</th>\n",
       "      <th>Glucose_concentration</th>\n",
       "      <th>Blood_pressure</th>\n",
       "      <th>Triceps</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>Pedigree</th>\n",
       "      <th>Age</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.352941</td>\n",
       "      <td>0.743719</td>\n",
       "      <td>0.590164</td>\n",
       "      <td>0.353535</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.500745</td>\n",
       "      <td>0.234415</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.058824</td>\n",
       "      <td>0.427136</td>\n",
       "      <td>0.540984</td>\n",
       "      <td>0.292929</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.396423</td>\n",
       "      <td>0.116567</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.470588</td>\n",
       "      <td>0.919598</td>\n",
       "      <td>0.524590</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.347243</td>\n",
       "      <td>0.253629</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.058824</td>\n",
       "      <td>0.447236</td>\n",
       "      <td>0.540984</td>\n",
       "      <td>0.232323</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.418778</td>\n",
       "      <td>0.038002</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.688442</td>\n",
       "      <td>0.327869</td>\n",
       "      <td>0.353535</td>\n",
       "      <td>0.198582</td>\n",
       "      <td>0.642325</td>\n",
       "      <td>0.943638</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Number_pregnant  Glucose_concentration  Blood_pressure   Triceps   Insulin  \\\n",
       "0         0.352941               0.743719        0.590164  0.353535  0.000000   \n",
       "1         0.058824               0.427136        0.540984  0.292929  0.000000   \n",
       "2         0.470588               0.919598        0.524590  0.000000  0.000000   \n",
       "3         0.058824               0.447236        0.540984  0.232323  0.111111   \n",
       "4         0.000000               0.688442        0.327869  0.353535  0.198582   \n",
       "\n",
       "        BMI  Pedigree  Age  Class  \n",
       "0  0.500745  0.234415   50      1  \n",
       "1  0.396423  0.116567   31      0  \n",
       "2  0.347243  0.253629   32      1  \n",
       "3  0.418778  0.038002   21      0  \n",
       "4  0.642325  0.943638   33      1  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diabetes.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature Columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Number_pregnant', 'Glucose_concentration', 'Blood_pressure', 'Triceps',\n",
       "       'Insulin', 'BMI', 'Pedigree', 'Age', 'Class'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diabetes.columns "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Continuous Features\n",
    "\n",
    "* Number of times pregnant\n",
    "* Plasma glucose concentration a 2 hours in an oral glucose tolerance test\n",
    "* Diastolic blood pressure (mm Hg)\n",
    "* Triceps skin fold thickness (mm)\n",
    "* 2-Hour serum insulin (mu U/ml)\n",
    "* Body mass index (weight in kg/(height in m)^2)\n",
    "* Diabetes pedigree function"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Encode Age to Age Groups"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a202fdf28>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "diabetes['Age'].hist(bins=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "bins = [0,30,50,70,100]\n",
    "labels =[0,1,2,3]\n",
    "diabetes[\"Age_buckets\"] = pd.cut(diabetes[\"Age\"],bins=bins, labels=labels, include_lowest=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Putting them together"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train Test Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Number_pregnant</th>\n",
       "      <th>Glucose_concentration</th>\n",
       "      <th>Blood_pressure</th>\n",
       "      <th>Triceps</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>Pedigree</th>\n",
       "      <th>Age</th>\n",
       "      <th>Class</th>\n",
       "      <th>Age_buckets</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.352941</td>\n",
       "      <td>0.743719</td>\n",
       "      <td>0.590164</td>\n",
       "      <td>0.353535</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.500745</td>\n",
       "      <td>0.234415</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.058824</td>\n",
       "      <td>0.427136</td>\n",
       "      <td>0.540984</td>\n",
       "      <td>0.292929</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.396423</td>\n",
       "      <td>0.116567</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.470588</td>\n",
       "      <td>0.919598</td>\n",
       "      <td>0.524590</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.347243</td>\n",
       "      <td>0.253629</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.058824</td>\n",
       "      <td>0.447236</td>\n",
       "      <td>0.540984</td>\n",
       "      <td>0.232323</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.418778</td>\n",
       "      <td>0.038002</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.688442</td>\n",
       "      <td>0.327869</td>\n",
       "      <td>0.353535</td>\n",
       "      <td>0.198582</td>\n",
       "      <td>0.642325</td>\n",
       "      <td>0.943638</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Number_pregnant  Glucose_concentration  Blood_pressure   Triceps   Insulin  \\\n",
       "0         0.352941               0.743719        0.590164  0.353535  0.000000   \n",
       "1         0.058824               0.427136        0.540984  0.292929  0.000000   \n",
       "2         0.470588               0.919598        0.524590  0.000000  0.000000   \n",
       "3         0.058824               0.447236        0.540984  0.232323  0.111111   \n",
       "4         0.000000               0.688442        0.327869  0.353535  0.198582   \n",
       "\n",
       "        BMI  Pedigree  Age  Class Age_buckets  \n",
       "0  0.500745  0.234415   50      1           1  \n",
       "1  0.396423  0.116567   31      0           1  \n",
       "2  0.347243  0.253629   32      1           1  \n",
       "3  0.418778  0.038002   21      0           0  \n",
       "4  0.642325  0.943638   33      1           1  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diabetes.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 768 entries, 0 to 767\n",
      "Data columns (total 9 columns):\n",
      "Number_pregnant          768 non-null float64\n",
      "Glucose_concentration    768 non-null float64\n",
      "Blood_pressure           768 non-null float64\n",
      "Triceps                  768 non-null float64\n",
      "Insulin                  768 non-null float64\n",
      "BMI                      768 non-null float64\n",
      "Pedigree                 768 non-null float64\n",
      "Age                      768 non-null int64\n",
      "Class                    768 non-null int64\n",
      "dtypes: float64(7), int64(2)\n",
      "memory usage: 54.1 KB\n"
     ]
    }
   ],
   "source": [
    "diabetes.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_data = diabetes.drop(['Age','Class'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "labels = diabetes['Class']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(x_data,labels,test_size=0.33, random_state=101)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(514,)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Keras"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense,Activation\n",
    "from tensorflow.keras.optimizers import SGD,Adam\n",
    "from tensorflow.keras.utils import to_categorical"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Sequential()\n",
    "#model.add(Dense(2,input_shape = (X_train.shape[1],),activation = 'softmax'))\n",
    "model.add(Dense(20,input_shape = (X_train.shape[1],), activation = 'relu'))\n",
    "model.add(Dense(10,activation = 'relu'))\n",
    "#model.add(Dense(10,activation = 'relu'))\n",
    "model.add(Dense(2, activation = 'softmax'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "adam = Adam(0.01)\n",
    "#sgd = SGD(0.005)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_binary_train= to_categorical(y_train)\n",
    "y_binary_test = to_categorical(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(loss = 'categorical_crossentropy', optimizer = adam, metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 514 samples, validate on 254 samples\n",
      "Epoch 1/500\n",
      "514/514 [==============================] - 2s 3ms/step - loss: 0.6771 - acc: 0.5973 - val_loss: 0.6458 - val_acc: 0.6575\n",
      "Epoch 2/500\n",
      "514/514 [==============================] - 0s 143us/step - loss: 0.6332 - acc: 0.6693 - val_loss: 0.6102 - val_acc: 0.6575\n",
      "Epoch 3/500\n",
      "514/514 [==============================] - 0s 135us/step - loss: 0.6144 - acc: 0.6654 - val_loss: 0.5986 - val_acc: 0.6850\n",
      "Epoch 4/500\n",
      "514/514 [==============================] - 0s 135us/step - loss: 0.6087 - acc: 0.6868 - val_loss: 0.6110 - val_acc: 0.6575\n",
      "Epoch 5/500\n",
      "514/514 [==============================] - 0s 133us/step - loss: 0.6005 - acc: 0.6595 - val_loss: 0.5691 - val_acc: 0.7244\n",
      "Epoch 6/500\n",
      "514/514 [==============================] - 0s 131us/step - loss: 0.5756 - acc: 0.6868 - val_loss: 0.5515 - val_acc: 0.7244\n",
      "Epoch 7/500\n",
      "514/514 [==============================] - 0s 134us/step - loss: 0.5591 - acc: 0.7101 - val_loss: 0.5481 - val_acc: 0.7087\n",
      "Epoch 8/500\n",
      "514/514 [==============================] - 0s 136us/step - loss: 0.5475 - acc: 0.7257 - val_loss: 0.5440 - val_acc: 0.7205\n",
      "Epoch 9/500\n",
      "514/514 [==============================] - 0s 143us/step - loss: 0.5420 - acc: 0.7276 - val_loss: 0.5106 - val_acc: 0.7638\n",
      "Epoch 10/500\n",
      "514/514 [==============================] - 0s 135us/step - loss: 0.5166 - acc: 0.7549 - val_loss: 0.4984 - val_acc: 0.7638\n",
      "Epoch 11/500\n",
      "514/514 [==============================] - 0s 128us/step - loss: 0.5150 - acc: 0.7374 - val_loss: 0.4908 - val_acc: 0.7598\n",
      "Epoch 12/500\n",
      "514/514 [==============================] - 0s 132us/step - loss: 0.5002 - acc: 0.7763 - val_loss: 0.4917 - val_acc: 0.7677\n",
      "Epoch 13/500\n",
      "514/514 [==============================] - 0s 134us/step - loss: 0.5019 - acc: 0.7607 - val_loss: 0.4777 - val_acc: 0.7835\n",
      "Epoch 14/500\n",
      "514/514 [==============================] - 0s 130us/step - loss: 0.4857 - acc: 0.7782 - val_loss: 0.4741 - val_acc: 0.7953\n",
      "Epoch 15/500\n",
      "514/514 [==============================] - 0s 175us/step - loss: 0.4853 - acc: 0.7743 - val_loss: 0.4618 - val_acc: 0.7913\n",
      "Epoch 16/500\n",
      "514/514 [==============================] - 0s 177us/step - loss: 0.4887 - acc: 0.7665 - val_loss: 0.4713 - val_acc: 0.7795\n",
      "Epoch 17/500\n",
      "514/514 [==============================] - 0s 134us/step - loss: 0.4708 - acc: 0.7860 - val_loss: 0.4622 - val_acc: 0.7953\n",
      "Epoch 18/500\n",
      "514/514 [==============================] - 0s 134us/step - loss: 0.4771 - acc: 0.7743 - val_loss: 0.4631 - val_acc: 0.7874\n",
      "Epoch 19/500\n",
      "514/514 [==============================] - 0s 149us/step - loss: 0.4730 - acc: 0.7763 - val_loss: 0.4587 - val_acc: 0.7874\n",
      "Epoch 20/500\n",
      "514/514 [==============================] - 0s 162us/step - loss: 0.4995 - acc: 0.7588 - val_loss: 0.4944 - val_acc: 0.7638\n",
      "Epoch 21/500\n",
      "514/514 [==============================] - 0s 140us/step - loss: 0.4671 - acc: 0.7879 - val_loss: 0.4759 - val_acc: 0.7520\n",
      "Epoch 22/500\n",
      "514/514 [==============================] - 0s 151us/step - loss: 0.4598 - acc: 0.7840 - val_loss: 0.4659 - val_acc: 0.7835\n",
      "Epoch 23/500\n",
      "514/514 [==============================] - 0s 132us/step - loss: 0.4680 - acc: 0.7918 - val_loss: 0.5035 - val_acc: 0.7480\n",
      "Epoch 24/500\n",
      "514/514 [==============================] - 0s 136us/step - loss: 0.4701 - acc: 0.7938 - val_loss: 0.4699 - val_acc: 0.7795\n",
      "Epoch 25/500\n",
      "514/514 [==============================] - 0s 128us/step - loss: 0.4729 - acc: 0.7743 - val_loss: 0.4624 - val_acc: 0.7913\n",
      "Epoch 26/500\n",
      "514/514 [==============================] - 0s 139us/step - loss: 0.4555 - acc: 0.7821 - val_loss: 0.4561 - val_acc: 0.7953\n",
      "Epoch 27/500\n",
      "514/514 [==============================] - 0s 130us/step - loss: 0.4881 - acc: 0.7704 - val_loss: 0.4867 - val_acc: 0.7480\n",
      "Epoch 28/500\n",
      "514/514 [==============================] - 0s 139us/step - loss: 0.4926 - acc: 0.7626 - val_loss: 0.5119 - val_acc: 0.7323\n",
      "Epoch 29/500\n",
      "514/514 [==============================] - 0s 141us/step - loss: 0.4936 - acc: 0.7490 - val_loss: 0.5523 - val_acc: 0.7323\n",
      "Epoch 30/500\n",
      "514/514 [==============================] - 0s 124us/step - loss: 0.5061 - acc: 0.7588 - val_loss: 0.4616 - val_acc: 0.7835\n",
      "Epoch 31/500\n",
      "514/514 [==============================] - 0s 122us/step - loss: 0.4653 - acc: 0.7918 - val_loss: 0.4774 - val_acc: 0.7638\n",
      "Epoch 32/500\n",
      "514/514 [==============================] - 0s 132us/step - loss: 0.4666 - acc: 0.7763 - val_loss: 0.4761 - val_acc: 0.7677\n",
      "Epoch 33/500\n",
      "514/514 [==============================] - 0s 140us/step - loss: 0.4531 - acc: 0.7743 - val_loss: 0.4576 - val_acc: 0.7835\n",
      "Epoch 34/500\n",
      "514/514 [==============================] - 0s 129us/step - loss: 0.4592 - acc: 0.7743 - val_loss: 0.4535 - val_acc: 0.8071\n",
      "Epoch 35/500\n",
      "514/514 [==============================] - 0s 140us/step - loss: 0.4578 - acc: 0.7840 - val_loss: 0.4591 - val_acc: 0.7874\n",
      "Epoch 36/500\n",
      "514/514 [==============================] - 0s 140us/step - loss: 0.4598 - acc: 0.7802 - val_loss: 0.4691 - val_acc: 0.7638\n",
      "Epoch 37/500\n",
      "514/514 [==============================] - 0s 127us/step - loss: 0.4684 - acc: 0.7899 - val_loss: 0.4772 - val_acc: 0.7953\n",
      "Epoch 38/500\n",
      "514/514 [==============================] - 0s 152us/step - loss: 0.4697 - acc: 0.7724 - val_loss: 0.4601 - val_acc: 0.7874\n",
      "Epoch 39/500\n",
      "514/514 [==============================] - 0s 143us/step - loss: 0.4549 - acc: 0.7782 - val_loss: 0.4841 - val_acc: 0.7402\n",
      "Epoch 40/500\n",
      "514/514 [==============================] - 0s 145us/step - loss: 0.4573 - acc: 0.7899 - val_loss: 0.4546 - val_acc: 0.7913\n",
      "Epoch 41/500\n",
      "514/514 [==============================] - 0s 156us/step - loss: 0.4444 - acc: 0.7782 - val_loss: 0.4705 - val_acc: 0.7756\n",
      "Epoch 42/500\n",
      "514/514 [==============================] - 0s 143us/step - loss: 0.4470 - acc: 0.7782 - val_loss: 0.4788 - val_acc: 0.7520\n",
      "Epoch 43/500\n",
      "514/514 [==============================] - 0s 147us/step - loss: 0.4471 - acc: 0.7957 - val_loss: 0.4646 - val_acc: 0.7835\n",
      "Epoch 44/500\n",
      "514/514 [==============================] - 0s 154us/step - loss: 0.4411 - acc: 0.7763 - val_loss: 0.4585 - val_acc: 0.7913\n",
      "Epoch 45/500\n",
      "514/514 [==============================] - 0s 147us/step - loss: 0.4476 - acc: 0.7977 - val_loss: 0.4620 - val_acc: 0.8189\n",
      "Epoch 46/500\n",
      "514/514 [==============================] - 0s 135us/step - loss: 0.4576 - acc: 0.7977 - val_loss: 0.4907 - val_acc: 0.7323\n",
      "Epoch 47/500\n",
      "514/514 [==============================] - 0s 136us/step - loss: 0.4365 - acc: 0.7977 - val_loss: 0.4677 - val_acc: 0.7913\n",
      "Epoch 48/500\n",
      "514/514 [==============================] - 0s 158us/step - loss: 0.4752 - acc: 0.7860 - val_loss: 0.4704 - val_acc: 0.7677\n",
      "Epoch 49/500\n",
      "514/514 [==============================] - 0s 141us/step - loss: 0.4556 - acc: 0.7860 - val_loss: 0.4643 - val_acc: 0.7953\n",
      "Epoch 50/500\n",
      "514/514 [==============================] - 0s 148us/step - loss: 0.4413 - acc: 0.7840 - val_loss: 0.5011 - val_acc: 0.7323\n",
      "Epoch 51/500\n",
      "514/514 [==============================] - 0s 156us/step - loss: 0.4535 - acc: 0.7957 - val_loss: 0.4705 - val_acc: 0.7795\n",
      "Epoch 52/500\n",
      "514/514 [==============================] - 0s 155us/step - loss: 0.4500 - acc: 0.7879 - val_loss: 0.4945 - val_acc: 0.7756\n",
      "Epoch 53/500\n",
      "514/514 [==============================] - 0s 137us/step - loss: 0.4735 - acc: 0.7763 - val_loss: 0.4782 - val_acc: 0.7598\n",
      "Epoch 54/500\n",
      "514/514 [==============================] - 0s 156us/step - loss: 0.4555 - acc: 0.7782 - val_loss: 0.4707 - val_acc: 0.7756\n",
      "Epoch 55/500\n",
      "514/514 [==============================] - 0s 136us/step - loss: 0.4412 - acc: 0.7860 - val_loss: 0.4580 - val_acc: 0.7992\n",
      "Epoch 56/500\n",
      "514/514 [==============================] - 0s 137us/step - loss: 0.4511 - acc: 0.7977 - val_loss: 0.4727 - val_acc: 0.7480\n",
      "Epoch 57/500\n",
      "514/514 [==============================] - 0s 131us/step - loss: 0.4472 - acc: 0.7860 - val_loss: 0.4668 - val_acc: 0.7992\n",
      "Epoch 58/500\n",
      "514/514 [==============================] - 0s 134us/step - loss: 0.4477 - acc: 0.7899 - val_loss: 0.4672 - val_acc: 0.7795\n",
      "Epoch 59/500\n",
      "514/514 [==============================] - 0s 122us/step - loss: 0.4461 - acc: 0.7821 - val_loss: 0.4576 - val_acc: 0.8071\n",
      "Epoch 60/500\n",
      "514/514 [==============================] - 0s 113us/step - loss: 0.4358 - acc: 0.7860 - val_loss: 0.4756 - val_acc: 0.7480\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 61/500\n",
      "514/514 [==============================] - 0s 116us/step - loss: 0.4350 - acc: 0.7938 - val_loss: 0.4667 - val_acc: 0.7992\n",
      "Epoch 62/500\n",
      "514/514 [==============================] - 0s 123us/step - loss: 0.4368 - acc: 0.7802 - val_loss: 0.4599 - val_acc: 0.8150\n",
      "Epoch 63/500\n",
      "514/514 [==============================] - 0s 113us/step - loss: 0.4513 - acc: 0.7860 - val_loss: 0.4763 - val_acc: 0.7598\n",
      "Epoch 64/500\n",
      "514/514 [==============================] - 0s 117us/step - loss: 0.4634 - acc: 0.7879 - val_loss: 0.4869 - val_acc: 0.7795\n",
      "Epoch 65/500\n",
      "514/514 [==============================] - 0s 119us/step - loss: 0.4462 - acc: 0.7821 - val_loss: 0.4665 - val_acc: 0.7913\n",
      "Epoch 66/500\n",
      "514/514 [==============================] - 0s 119us/step - loss: 0.4480 - acc: 0.7879 - val_loss: 0.4773 - val_acc: 0.7480\n",
      "Epoch 67/500\n",
      "514/514 [==============================] - 0s 112us/step - loss: 0.4626 - acc: 0.7957 - val_loss: 0.4770 - val_acc: 0.7677\n",
      "Epoch 68/500\n",
      "514/514 [==============================] - 0s 118us/step - loss: 0.4416 - acc: 0.7957 - val_loss: 0.4667 - val_acc: 0.8150\n",
      "Epoch 69/500\n",
      "514/514 [==============================] - 0s 131us/step - loss: 0.4416 - acc: 0.7879 - val_loss: 0.4722 - val_acc: 0.7598\n",
      "Epoch 70/500\n",
      "514/514 [==============================] - 0s 119us/step - loss: 0.4922 - acc: 0.7549 - val_loss: 0.4848 - val_acc: 0.7835\n",
      "Epoch 71/500\n",
      "514/514 [==============================] - 0s 123us/step - loss: 0.4512 - acc: 0.7860 - val_loss: 0.4886 - val_acc: 0.7402\n",
      "Epoch 72/500\n",
      "514/514 [==============================] - 0s 129us/step - loss: 0.4492 - acc: 0.7938 - val_loss: 0.4622 - val_acc: 0.7992\n",
      "Epoch 73/500\n",
      "514/514 [==============================] - 0s 120us/step - loss: 0.4440 - acc: 0.7821 - val_loss: 0.4588 - val_acc: 0.8031\n",
      "Epoch 74/500\n",
      "514/514 [==============================] - 0s 114us/step - loss: 0.4372 - acc: 0.7918 - val_loss: 0.4619 - val_acc: 0.7874\n",
      "Epoch 75/500\n",
      "514/514 [==============================] - 0s 122us/step - loss: 0.4321 - acc: 0.7899 - val_loss: 0.4620 - val_acc: 0.7913\n",
      "Epoch 76/500\n",
      "514/514 [==============================] - 0s 117us/step - loss: 0.4413 - acc: 0.7840 - val_loss: 0.5009 - val_acc: 0.7402\n",
      "Epoch 77/500\n",
      "514/514 [==============================] - 0s 114us/step - loss: 0.4452 - acc: 0.7724 - val_loss: 0.4705 - val_acc: 0.7677\n",
      "Epoch 78/500\n",
      "514/514 [==============================] - 0s 121us/step - loss: 0.4569 - acc: 0.7860 - val_loss: 0.4701 - val_acc: 0.7835\n",
      "Epoch 79/500\n",
      "514/514 [==============================] - 0s 114us/step - loss: 0.4446 - acc: 0.7899 - val_loss: 0.4618 - val_acc: 0.7953\n",
      "Epoch 80/500\n",
      "514/514 [==============================] - 0s 118us/step - loss: 0.4366 - acc: 0.7918 - val_loss: 0.4640 - val_acc: 0.7795\n",
      "Epoch 81/500\n",
      "514/514 [==============================] - 0s 132us/step - loss: 0.4412 - acc: 0.7918 - val_loss: 0.5067 - val_acc: 0.7205\n",
      "Epoch 82/500\n",
      "514/514 [==============================] - 0s 121us/step - loss: 0.4457 - acc: 0.7957 - val_loss: 0.4653 - val_acc: 0.7756\n",
      "Epoch 83/500\n",
      "514/514 [==============================] - 0s 130us/step - loss: 0.4329 - acc: 0.7840 - val_loss: 0.4669 - val_acc: 0.7874\n",
      "Epoch 84/500\n",
      "514/514 [==============================] - 0s 125us/step - loss: 0.4419 - acc: 0.8035 - val_loss: 0.4933 - val_acc: 0.7953\n",
      "Epoch 85/500\n",
      "514/514 [==============================] - 0s 136us/step - loss: 0.4457 - acc: 0.7860 - val_loss: 0.4835 - val_acc: 0.7480\n",
      "Epoch 86/500\n",
      "514/514 [==============================] - 0s 132us/step - loss: 0.4310 - acc: 0.7821 - val_loss: 0.4669 - val_acc: 0.7835\n",
      "Epoch 87/500\n",
      "514/514 [==============================] - 0s 148us/step - loss: 0.4313 - acc: 0.7938 - val_loss: 0.4787 - val_acc: 0.7559\n",
      "Epoch 88/500\n",
      "514/514 [==============================] - 0s 135us/step - loss: 0.4299 - acc: 0.7802 - val_loss: 0.4726 - val_acc: 0.7756\n",
      "Epoch 89/500\n",
      "514/514 [==============================] - 0s 138us/step - loss: 0.4335 - acc: 0.7938 - val_loss: 0.4760 - val_acc: 0.7520\n",
      "Epoch 90/500\n",
      "514/514 [==============================] - 0s 135us/step - loss: 0.4592 - acc: 0.7802 - val_loss: 0.4807 - val_acc: 0.7913\n",
      "Epoch 91/500\n",
      "514/514 [==============================] - 0s 142us/step - loss: 0.4359 - acc: 0.7938 - val_loss: 0.4739 - val_acc: 0.7835\n",
      "Epoch 92/500\n",
      "514/514 [==============================] - 0s 125us/step - loss: 0.4325 - acc: 0.7957 - val_loss: 0.4683 - val_acc: 0.7756\n",
      "Epoch 93/500\n",
      "514/514 [==============================] - 0s 128us/step - loss: 0.4280 - acc: 0.7938 - val_loss: 0.4662 - val_acc: 0.8071\n",
      "Epoch 94/500\n",
      "514/514 [==============================] - 0s 140us/step - loss: 0.4331 - acc: 0.7918 - val_loss: 0.4679 - val_acc: 0.7953\n",
      "Epoch 95/500\n",
      "514/514 [==============================] - 0s 139us/step - loss: 0.4314 - acc: 0.7899 - val_loss: 0.4738 - val_acc: 0.7835\n",
      "Epoch 96/500\n",
      "514/514 [==============================] - 0s 152us/step - loss: 0.4272 - acc: 0.7840 - val_loss: 0.4746 - val_acc: 0.7835\n",
      "Epoch 97/500\n",
      "514/514 [==============================] - 0s 140us/step - loss: 0.4252 - acc: 0.7879 - val_loss: 0.4761 - val_acc: 0.7835\n",
      "Epoch 98/500\n",
      "514/514 [==============================] - 0s 138us/step - loss: 0.4259 - acc: 0.7957 - val_loss: 0.4759 - val_acc: 0.8031\n",
      "Epoch 99/500\n",
      "514/514 [==============================] - 0s 140us/step - loss: 0.4238 - acc: 0.7977 - val_loss: 0.4864 - val_acc: 0.7953\n",
      "Epoch 100/500\n",
      "514/514 [==============================] - 0s 139us/step - loss: 0.4332 - acc: 0.7802 - val_loss: 0.4694 - val_acc: 0.7835\n",
      "Epoch 101/500\n",
      "514/514 [==============================] - 0s 131us/step - loss: 0.4345 - acc: 0.7782 - val_loss: 0.4871 - val_acc: 0.7402\n",
      "Epoch 102/500\n",
      "514/514 [==============================] - 0s 139us/step - loss: 0.4287 - acc: 0.7957 - val_loss: 0.4835 - val_acc: 0.7520\n",
      "Epoch 103/500\n",
      "514/514 [==============================] - 0s 139us/step - loss: 0.4311 - acc: 0.7899 - val_loss: 0.5113 - val_acc: 0.7441\n",
      "Epoch 104/500\n",
      "514/514 [==============================] - 0s 140us/step - loss: 0.4768 - acc: 0.7763 - val_loss: 0.5059 - val_acc: 0.7795\n",
      "Epoch 105/500\n",
      "514/514 [==============================] - 0s 133us/step - loss: 0.4427 - acc: 0.7938 - val_loss: 0.4872 - val_acc: 0.7992\n",
      "Epoch 106/500\n",
      "514/514 [==============================] - 0s 135us/step - loss: 0.4320 - acc: 0.7918 - val_loss: 0.4725 - val_acc: 0.8071\n",
      "Epoch 107/500\n",
      "514/514 [==============================] - 0s 130us/step - loss: 0.4313 - acc: 0.7821 - val_loss: 0.4808 - val_acc: 0.7953\n",
      "Epoch 108/500\n",
      "514/514 [==============================] - 0s 132us/step - loss: 0.4277 - acc: 0.7957 - val_loss: 0.4897 - val_acc: 0.7874\n",
      "Epoch 109/500\n",
      "514/514 [==============================] - 0s 142us/step - loss: 0.4663 - acc: 0.7782 - val_loss: 0.5064 - val_acc: 0.7402\n",
      "Epoch 110/500\n",
      "514/514 [==============================] - 0s 131us/step - loss: 0.4422 - acc: 0.7957 - val_loss: 0.4749 - val_acc: 0.7717\n",
      "Epoch 111/500\n",
      "514/514 [==============================] - 0s 130us/step - loss: 0.4298 - acc: 0.7977 - val_loss: 0.4605 - val_acc: 0.7992\n",
      "Epoch 112/500\n",
      "514/514 [==============================] - 0s 130us/step - loss: 0.4334 - acc: 0.7821 - val_loss: 0.4644 - val_acc: 0.7874\n",
      "Epoch 113/500\n",
      "514/514 [==============================] - 0s 138us/step - loss: 0.4287 - acc: 0.7918 - val_loss: 0.4692 - val_acc: 0.7795\n",
      "Epoch 114/500\n",
      "514/514 [==============================] - 0s 130us/step - loss: 0.4239 - acc: 0.8035 - val_loss: 0.4840 - val_acc: 0.7402\n",
      "Epoch 115/500\n",
      "514/514 [==============================] - 0s 132us/step - loss: 0.4415 - acc: 0.7938 - val_loss: 0.4793 - val_acc: 0.7598\n",
      "Epoch 116/500\n",
      "514/514 [==============================] - 0s 138us/step - loss: 0.4366 - acc: 0.7918 - val_loss: 0.4752 - val_acc: 0.7756\n",
      "Epoch 117/500\n",
      "514/514 [==============================] - 0s 139us/step - loss: 0.4736 - acc: 0.7568 - val_loss: 0.5030 - val_acc: 0.7795\n",
      "Epoch 118/500\n",
      "514/514 [==============================] - 0s 140us/step - loss: 0.4567 - acc: 0.7704 - val_loss: 0.4806 - val_acc: 0.7677\n",
      "Epoch 119/500\n",
      "514/514 [==============================] - 0s 130us/step - loss: 0.4395 - acc: 0.7879 - val_loss: 0.4810 - val_acc: 0.7795\n",
      "Epoch 120/500\n",
      "514/514 [==============================] - 0s 130us/step - loss: 0.4525 - acc: 0.7957 - val_loss: 0.5076 - val_acc: 0.7480\n",
      "Epoch 121/500\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "514/514 [==============================] - 0s 132us/step - loss: 0.4354 - acc: 0.7860 - val_loss: 0.4786 - val_acc: 0.7835\n",
      "Epoch 122/500\n",
      "514/514 [==============================] - 0s 129us/step - loss: 0.4681 - acc: 0.7607 - val_loss: 0.4930 - val_acc: 0.7677\n",
      "Epoch 123/500\n",
      "514/514 [==============================] - 0s 141us/step - loss: 0.4510 - acc: 0.7685 - val_loss: 0.4933 - val_acc: 0.7598\n",
      "Epoch 124/500\n",
      "514/514 [==============================] - 0s 128us/step - loss: 0.4481 - acc: 0.7860 - val_loss: 0.4777 - val_acc: 0.7835\n",
      "Epoch 125/500\n",
      "514/514 [==============================] - 0s 129us/step - loss: 0.4380 - acc: 0.7763 - val_loss: 0.4817 - val_acc: 0.7953\n",
      "Epoch 126/500\n",
      "514/514 [==============================] - 0s 129us/step - loss: 0.4366 - acc: 0.7918 - val_loss: 0.4811 - val_acc: 0.7835\n",
      "Epoch 127/500\n",
      "514/514 [==============================] - 0s 128us/step - loss: 0.4327 - acc: 0.7879 - val_loss: 0.5006 - val_acc: 0.7402\n",
      "Epoch 128/500\n",
      "514/514 [==============================] - 0s 133us/step - loss: 0.4272 - acc: 0.8016 - val_loss: 0.4861 - val_acc: 0.7756\n",
      "Epoch 129/500\n",
      "514/514 [==============================] - 0s 124us/step - loss: 0.4207 - acc: 0.7996 - val_loss: 0.4883 - val_acc: 0.7953\n",
      "Epoch 130/500\n",
      "514/514 [==============================] - 0s 123us/step - loss: 0.4331 - acc: 0.7899 - val_loss: 0.4885 - val_acc: 0.7874\n",
      "Epoch 131/500\n",
      "514/514 [==============================] - 0s 119us/step - loss: 0.4360 - acc: 0.7840 - val_loss: 0.4869 - val_acc: 0.7874\n",
      "Epoch 132/500\n",
      "514/514 [==============================] - 0s 113us/step - loss: 0.4217 - acc: 0.7938 - val_loss: 0.4938 - val_acc: 0.7795\n",
      "Epoch 133/500\n",
      "514/514 [==============================] - 0s 121us/step - loss: 0.4292 - acc: 0.7840 - val_loss: 0.4937 - val_acc: 0.7835\n",
      "Epoch 134/500\n",
      "514/514 [==============================] - 0s 120us/step - loss: 0.4265 - acc: 0.7821 - val_loss: 0.4954 - val_acc: 0.7874\n",
      "Epoch 135/500\n",
      "514/514 [==============================] - 0s 114us/step - loss: 0.4414 - acc: 0.7938 - val_loss: 0.5546 - val_acc: 0.7283\n",
      "Epoch 136/500\n",
      "514/514 [==============================] - 0s 122us/step - loss: 0.4346 - acc: 0.7996 - val_loss: 0.4782 - val_acc: 0.7835\n",
      "Epoch 137/500\n",
      "514/514 [==============================] - 0s 126us/step - loss: 0.4201 - acc: 0.7879 - val_loss: 0.4905 - val_acc: 0.7795\n",
      "Epoch 138/500\n",
      "514/514 [==============================] - 0s 122us/step - loss: 0.4215 - acc: 0.7938 - val_loss: 0.5109 - val_acc: 0.7441\n",
      "Epoch 139/500\n",
      "514/514 [==============================] - 0s 120us/step - loss: 0.4236 - acc: 0.7879 - val_loss: 0.4898 - val_acc: 0.7756\n",
      "Epoch 140/500\n",
      "514/514 [==============================] - 0s 132us/step - loss: 0.4261 - acc: 0.7957 - val_loss: 0.4998 - val_acc: 0.7953\n",
      "Epoch 141/500\n",
      "514/514 [==============================] - 0s 130us/step - loss: 0.4218 - acc: 0.7802 - val_loss: 0.4901 - val_acc: 0.7677\n",
      "Epoch 142/500\n",
      "514/514 [==============================] - 0s 136us/step - loss: 0.4205 - acc: 0.7860 - val_loss: 0.4920 - val_acc: 0.7874\n",
      "Epoch 143/500\n",
      "514/514 [==============================] - 0s 133us/step - loss: 0.4150 - acc: 0.7918 - val_loss: 0.4963 - val_acc: 0.7677\n",
      "Epoch 144/500\n",
      "514/514 [==============================] - 0s 132us/step - loss: 0.4175 - acc: 0.7899 - val_loss: 0.5015 - val_acc: 0.7480\n",
      "Epoch 145/500\n",
      "514/514 [==============================] - 0s 124us/step - loss: 0.4245 - acc: 0.7879 - val_loss: 0.4965 - val_acc: 0.7598\n",
      "Epoch 146/500\n",
      "514/514 [==============================] - 0s 148us/step - loss: 0.4381 - acc: 0.7840 - val_loss: 0.4991 - val_acc: 0.7953\n",
      "Epoch 147/500\n",
      "514/514 [==============================] - 0s 136us/step - loss: 0.4298 - acc: 0.7782 - val_loss: 0.5067 - val_acc: 0.7874\n",
      "Epoch 148/500\n",
      "514/514 [==============================] - 0s 130us/step - loss: 0.4314 - acc: 0.7938 - val_loss: 0.4936 - val_acc: 0.7756\n",
      "Epoch 149/500\n",
      "514/514 [==============================] - 0s 129us/step - loss: 0.4223 - acc: 0.7957 - val_loss: 0.5059 - val_acc: 0.7480\n",
      "Epoch 150/500\n",
      "514/514 [==============================] - 0s 129us/step - loss: 0.4250 - acc: 0.7996 - val_loss: 0.5235 - val_acc: 0.7756\n",
      "Epoch 151/500\n",
      "514/514 [==============================] - 0s 129us/step - loss: 0.4327 - acc: 0.7840 - val_loss: 0.4937 - val_acc: 0.7598\n",
      "Epoch 152/500\n",
      "514/514 [==============================] - 0s 130us/step - loss: 0.4275 - acc: 0.8152 - val_loss: 0.5047 - val_acc: 0.7638\n",
      "Epoch 153/500\n",
      "514/514 [==============================] - 0s 134us/step - loss: 0.4246 - acc: 0.7918 - val_loss: 0.5037 - val_acc: 0.7835\n",
      "Epoch 154/500\n",
      "514/514 [==============================] - 0s 141us/step - loss: 0.4260 - acc: 0.7977 - val_loss: 0.4924 - val_acc: 0.7638\n",
      "Epoch 155/500\n",
      "514/514 [==============================] - 0s 146us/step - loss: 0.4250 - acc: 0.7918 - val_loss: 0.5012 - val_acc: 0.7717\n",
      "Epoch 156/500\n",
      "514/514 [==============================] - 0s 124us/step - loss: 0.4316 - acc: 0.7860 - val_loss: 0.5144 - val_acc: 0.7441\n",
      "Epoch 157/500\n",
      "514/514 [==============================] - 0s 132us/step - loss: 0.4211 - acc: 0.7977 - val_loss: 0.5004 - val_acc: 0.7598\n",
      "Epoch 158/500\n",
      "514/514 [==============================] - 0s 131us/step - loss: 0.4193 - acc: 0.7938 - val_loss: 0.5018 - val_acc: 0.7638\n",
      "Epoch 159/500\n",
      "514/514 [==============================] - 0s 129us/step - loss: 0.4123 - acc: 0.7977 - val_loss: 0.4977 - val_acc: 0.7717\n",
      "Epoch 160/500\n",
      "514/514 [==============================] - 0s 120us/step - loss: 0.4162 - acc: 0.8152 - val_loss: 0.5560 - val_acc: 0.7087\n",
      "Epoch 161/500\n",
      "514/514 [==============================] - 0s 127us/step - loss: 0.4364 - acc: 0.7977 - val_loss: 0.4953 - val_acc: 0.7953\n",
      "Epoch 162/500\n",
      "514/514 [==============================] - 0s 135us/step - loss: 0.4183 - acc: 0.7977 - val_loss: 0.5152 - val_acc: 0.7835\n",
      "Epoch 163/500\n",
      "514/514 [==============================] - 0s 120us/step - loss: 0.4553 - acc: 0.7821 - val_loss: 0.5009 - val_acc: 0.7717\n",
      "Epoch 164/500\n",
      "514/514 [==============================] - 0s 123us/step - loss: 0.4155 - acc: 0.7938 - val_loss: 0.5041 - val_acc: 0.7677\n",
      "Epoch 165/500\n",
      "514/514 [==============================] - 0s 138us/step - loss: 0.4188 - acc: 0.7957 - val_loss: 0.5035 - val_acc: 0.7795\n",
      "Epoch 166/500\n",
      "514/514 [==============================] - 0s 154us/step - loss: 0.4219 - acc: 0.8132 - val_loss: 0.5539 - val_acc: 0.7087\n",
      "Epoch 167/500\n",
      "514/514 [==============================] - 0s 125us/step - loss: 0.4402 - acc: 0.7918 - val_loss: 0.5519 - val_acc: 0.7205\n",
      "Epoch 168/500\n",
      "514/514 [==============================] - 0s 120us/step - loss: 0.5148 - acc: 0.7335 - val_loss: 0.5239 - val_acc: 0.7795\n",
      "Epoch 169/500\n",
      "514/514 [==============================] - 0s 152us/step - loss: 0.4475 - acc: 0.7782 - val_loss: 0.5112 - val_acc: 0.7598\n",
      "Epoch 170/500\n",
      "514/514 [==============================] - 0s 122us/step - loss: 0.4521 - acc: 0.7879 - val_loss: 0.5064 - val_acc: 0.7638\n",
      "Epoch 171/500\n",
      "514/514 [==============================] - 0s 122us/step - loss: 0.4443 - acc: 0.7938 - val_loss: 0.5135 - val_acc: 0.7520\n",
      "Epoch 172/500\n",
      "514/514 [==============================] - 0s 146us/step - loss: 0.4284 - acc: 0.7860 - val_loss: 0.5063 - val_acc: 0.7717\n",
      "Epoch 173/500\n",
      "514/514 [==============================] - 0s 138us/step - loss: 0.4212 - acc: 0.7977 - val_loss: 0.5110 - val_acc: 0.7795\n",
      "Epoch 174/500\n",
      "514/514 [==============================] - 0s 129us/step - loss: 0.4313 - acc: 0.7860 - val_loss: 0.5211 - val_acc: 0.7362\n",
      "Epoch 175/500\n",
      "514/514 [==============================] - 0s 137us/step - loss: 0.4351 - acc: 0.7879 - val_loss: 0.5045 - val_acc: 0.7835\n",
      "Epoch 176/500\n",
      "514/514 [==============================] - 0s 137us/step - loss: 0.4325 - acc: 0.7899 - val_loss: 0.5272 - val_acc: 0.7362\n",
      "Epoch 177/500\n",
      "514/514 [==============================] - 0s 128us/step - loss: 0.4346 - acc: 0.7860 - val_loss: 0.5035 - val_acc: 0.7795\n",
      "Epoch 178/500\n",
      "514/514 [==============================] - 0s 122us/step - loss: 0.4330 - acc: 0.7840 - val_loss: 0.5078 - val_acc: 0.7677\n",
      "Epoch 179/500\n",
      "514/514 [==============================] - 0s 115us/step - loss: 0.4243 - acc: 0.7840 - val_loss: 0.5147 - val_acc: 0.7480\n",
      "Epoch 180/500\n",
      "514/514 [==============================] - 0s 118us/step - loss: 0.4180 - acc: 0.7938 - val_loss: 0.5142 - val_acc: 0.7677\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 181/500\n",
      "514/514 [==============================] - 0s 130us/step - loss: 0.4193 - acc: 0.7899 - val_loss: 0.5066 - val_acc: 0.7638\n",
      "Epoch 182/500\n",
      "514/514 [==============================] - 0s 120us/step - loss: 0.4125 - acc: 0.8016 - val_loss: 0.5261 - val_acc: 0.7756\n",
      "Epoch 183/500\n",
      "514/514 [==============================] - 0s 121us/step - loss: 0.4372 - acc: 0.7840 - val_loss: 0.5031 - val_acc: 0.7795\n",
      "Epoch 184/500\n",
      "514/514 [==============================] - 0s 116us/step - loss: 0.4265 - acc: 0.7743 - val_loss: 0.5043 - val_acc: 0.7756\n",
      "Epoch 185/500\n",
      "514/514 [==============================] - 0s 130us/step - loss: 0.4202 - acc: 0.7918 - val_loss: 0.4907 - val_acc: 0.7756\n",
      "Epoch 186/500\n",
      "514/514 [==============================] - 0s 119us/step - loss: 0.4132 - acc: 0.7918 - val_loss: 0.4941 - val_acc: 0.7756\n",
      "Epoch 187/500\n",
      "514/514 [==============================] - 0s 120us/step - loss: 0.4253 - acc: 0.7918 - val_loss: 0.4882 - val_acc: 0.7795\n",
      "Epoch 188/500\n",
      "514/514 [==============================] - 0s 127us/step - loss: 0.4273 - acc: 0.7977 - val_loss: 0.4916 - val_acc: 0.7874\n",
      "Epoch 189/500\n",
      "514/514 [==============================] - 0s 119us/step - loss: 0.4300 - acc: 0.7957 - val_loss: 0.5046 - val_acc: 0.7913\n",
      "Epoch 190/500\n",
      "514/514 [==============================] - 0s 116us/step - loss: 0.4158 - acc: 0.7899 - val_loss: 0.5098 - val_acc: 0.7795\n",
      "Epoch 191/500\n",
      "514/514 [==============================] - 0s 121us/step - loss: 0.4148 - acc: 0.7938 - val_loss: 0.5610 - val_acc: 0.7165\n",
      "Epoch 192/500\n",
      "514/514 [==============================] - 0s 126us/step - loss: 0.4325 - acc: 0.7918 - val_loss: 0.5136 - val_acc: 0.7835\n",
      "Epoch 193/500\n",
      "514/514 [==============================] - 0s 129us/step - loss: 0.4211 - acc: 0.7899 - val_loss: 0.5166 - val_acc: 0.7874\n",
      "Epoch 194/500\n",
      "514/514 [==============================] - 0s 129us/step - loss: 0.4160 - acc: 0.7918 - val_loss: 0.5270 - val_acc: 0.7756\n",
      "Epoch 195/500\n",
      "514/514 [==============================] - 0s 129us/step - loss: 0.4273 - acc: 0.7899 - val_loss: 0.5599 - val_acc: 0.7165\n",
      "Epoch 196/500\n",
      "514/514 [==============================] - 0s 129us/step - loss: 0.4382 - acc: 0.7860 - val_loss: 0.5154 - val_acc: 0.7795\n",
      "Epoch 197/500\n",
      "514/514 [==============================] - 0s 122us/step - loss: 0.4327 - acc: 0.7899 - val_loss: 0.4952 - val_acc: 0.7717\n",
      "Epoch 198/500\n",
      "514/514 [==============================] - 0s 147us/step - loss: 0.4301 - acc: 0.7899 - val_loss: 0.5222 - val_acc: 0.7913\n",
      "Epoch 199/500\n",
      "514/514 [==============================] - 0s 123us/step - loss: 0.4146 - acc: 0.8054 - val_loss: 0.5170 - val_acc: 0.7717\n",
      "Epoch 200/500\n",
      "514/514 [==============================] - 0s 133us/step - loss: 0.4257 - acc: 0.7899 - val_loss: 0.5209 - val_acc: 0.7835\n",
      "Epoch 201/500\n",
      "514/514 [==============================] - 0s 123us/step - loss: 0.4125 - acc: 0.7899 - val_loss: 0.5117 - val_acc: 0.7677\n",
      "Epoch 202/500\n",
      "514/514 [==============================] - 0s 149us/step - loss: 0.4188 - acc: 0.7860 - val_loss: 0.5124 - val_acc: 0.7874\n",
      "Epoch 203/500\n",
      "514/514 [==============================] - 0s 132us/step - loss: 0.4190 - acc: 0.7957 - val_loss: 0.5171 - val_acc: 0.7913\n",
      "Epoch 204/500\n",
      "514/514 [==============================] - 0s 121us/step - loss: 0.4187 - acc: 0.7996 - val_loss: 0.5304 - val_acc: 0.7362\n",
      "Epoch 205/500\n",
      "514/514 [==============================] - 0s 131us/step - loss: 0.4333 - acc: 0.8074 - val_loss: 0.5135 - val_acc: 0.7677\n",
      "Epoch 206/500\n",
      "514/514 [==============================] - 0s 131us/step - loss: 0.4473 - acc: 0.7860 - val_loss: 0.5273 - val_acc: 0.7677\n",
      "Epoch 207/500\n",
      "514/514 [==============================] - 0s 122us/step - loss: 0.4419 - acc: 0.7821 - val_loss: 0.5266 - val_acc: 0.7244\n",
      "Epoch 208/500\n",
      "514/514 [==============================] - 0s 131us/step - loss: 0.4128 - acc: 0.7996 - val_loss: 0.5197 - val_acc: 0.7874\n",
      "Epoch 209/500\n",
      "514/514 [==============================] - 0s 132us/step - loss: 0.4169 - acc: 0.7957 - val_loss: 0.5188 - val_acc: 0.7559\n",
      "Epoch 210/500\n",
      "514/514 [==============================] - 0s 130us/step - loss: 0.4148 - acc: 0.7977 - val_loss: 0.5146 - val_acc: 0.7520\n",
      "Epoch 211/500\n",
      "514/514 [==============================] - 0s 124us/step - loss: 0.4192 - acc: 0.7879 - val_loss: 0.5250 - val_acc: 0.7638\n",
      "Epoch 212/500\n",
      "514/514 [==============================] - 0s 129us/step - loss: 0.4122 - acc: 0.8016 - val_loss: 0.5204 - val_acc: 0.7559\n",
      "Epoch 213/500\n",
      "514/514 [==============================] - 0s 126us/step - loss: 0.4067 - acc: 0.8054 - val_loss: 0.5232 - val_acc: 0.7756\n",
      "Epoch 214/500\n",
      "514/514 [==============================] - 0s 149us/step - loss: 0.4061 - acc: 0.8016 - val_loss: 0.5160 - val_acc: 0.7874\n",
      "Epoch 215/500\n",
      "514/514 [==============================] - 0s 116us/step - loss: 0.4298 - acc: 0.7879 - val_loss: 0.5154 - val_acc: 0.7717\n",
      "Epoch 216/500\n",
      "514/514 [==============================] - 0s 118us/step - loss: 0.4161 - acc: 0.7938 - val_loss: 0.5428 - val_acc: 0.7362\n",
      "Epoch 217/500\n",
      "514/514 [==============================] - 0s 119us/step - loss: 0.4166 - acc: 0.7938 - val_loss: 0.5179 - val_acc: 0.7402\n",
      "Epoch 218/500\n",
      "514/514 [==============================] - 0s 134us/step - loss: 0.4290 - acc: 0.7938 - val_loss: 0.5221 - val_acc: 0.7835\n",
      "Epoch 219/500\n",
      "514/514 [==============================] - 0s 136us/step - loss: 0.4377 - acc: 0.7899 - val_loss: 0.5040 - val_acc: 0.7559\n",
      "Epoch 220/500\n",
      "514/514 [==============================] - 0s 119us/step - loss: 0.4127 - acc: 0.7996 - val_loss: 0.4986 - val_acc: 0.7598\n",
      "Epoch 221/500\n",
      "514/514 [==============================] - 0s 123us/step - loss: 0.4250 - acc: 0.7860 - val_loss: 0.5082 - val_acc: 0.7323\n",
      "Epoch 222/500\n",
      "514/514 [==============================] - 0s 138us/step - loss: 0.4146 - acc: 0.7918 - val_loss: 0.5017 - val_acc: 0.7835\n",
      "Epoch 223/500\n",
      "514/514 [==============================] - 0s 138us/step - loss: 0.4147 - acc: 0.7977 - val_loss: 0.5058 - val_acc: 0.7638\n",
      "Epoch 224/500\n",
      "514/514 [==============================] - 0s 154us/step - loss: 0.4119 - acc: 0.7977 - val_loss: 0.5506 - val_acc: 0.7126\n",
      "Epoch 225/500\n",
      "514/514 [==============================] - 0s 123us/step - loss: 0.4114 - acc: 0.8035 - val_loss: 0.5286 - val_acc: 0.7559\n",
      "Epoch 226/500\n",
      "514/514 [==============================] - 0s 134us/step - loss: 0.4109 - acc: 0.7957 - val_loss: 0.5464 - val_acc: 0.7126\n",
      "Epoch 227/500\n",
      "514/514 [==============================] - 0s 141us/step - loss: 0.4345 - acc: 0.7957 - val_loss: 0.5265 - val_acc: 0.7638\n",
      "Epoch 228/500\n",
      "514/514 [==============================] - 0s 139us/step - loss: 0.4238 - acc: 0.7977 - val_loss: 0.5232 - val_acc: 0.7520\n",
      "Epoch 229/500\n",
      "514/514 [==============================] - 0s 135us/step - loss: 0.4177 - acc: 0.8035 - val_loss: 0.5386 - val_acc: 0.7559\n",
      "Epoch 230/500\n",
      "514/514 [==============================] - 0s 137us/step - loss: 0.4019 - acc: 0.8152 - val_loss: 0.5147 - val_acc: 0.7756\n",
      "Epoch 231/500\n",
      "514/514 [==============================] - 0s 138us/step - loss: 0.4094 - acc: 0.8016 - val_loss: 0.5288 - val_acc: 0.7598\n",
      "Epoch 232/500\n",
      "514/514 [==============================] - 0s 178us/step - loss: 0.3991 - acc: 0.8074 - val_loss: 0.5482 - val_acc: 0.7913\n",
      "Epoch 233/500\n",
      "514/514 [==============================] - 0s 145us/step - loss: 0.4351 - acc: 0.7938 - val_loss: 0.5536 - val_acc: 0.7165\n",
      "Epoch 234/500\n",
      "514/514 [==============================] - 0s 176us/step - loss: 0.4214 - acc: 0.7918 - val_loss: 0.5130 - val_acc: 0.7638\n",
      "Epoch 235/500\n",
      "514/514 [==============================] - 0s 166us/step - loss: 0.4178 - acc: 0.7996 - val_loss: 0.5271 - val_acc: 0.7087\n",
      "Epoch 236/500\n",
      "514/514 [==============================] - 0s 159us/step - loss: 0.4066 - acc: 0.7938 - val_loss: 0.5071 - val_acc: 0.7677\n",
      "Epoch 237/500\n",
      "514/514 [==============================] - 0s 159us/step - loss: 0.4045 - acc: 0.8113 - val_loss: 0.4995 - val_acc: 0.7717\n",
      "Epoch 238/500\n",
      "514/514 [==============================] - 0s 154us/step - loss: 0.4140 - acc: 0.7938 - val_loss: 0.5138 - val_acc: 0.7244\n",
      "Epoch 239/500\n",
      "514/514 [==============================] - 0s 126us/step - loss: 0.4141 - acc: 0.8132 - val_loss: 0.4974 - val_acc: 0.7559\n",
      "Epoch 240/500\n",
      "514/514 [==============================] - 0s 120us/step - loss: 0.4069 - acc: 0.8074 - val_loss: 0.5026 - val_acc: 0.7795\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 241/500\n",
      "514/514 [==============================] - 0s 120us/step - loss: 0.4269 - acc: 0.7918 - val_loss: 0.5328 - val_acc: 0.7323\n",
      "Epoch 242/500\n",
      "514/514 [==============================] - 0s 115us/step - loss: 0.4268 - acc: 0.7860 - val_loss: 0.5178 - val_acc: 0.7480\n",
      "Epoch 243/500\n",
      "514/514 [==============================] - 0s 111us/step - loss: 0.4078 - acc: 0.7938 - val_loss: 0.4998 - val_acc: 0.7717\n",
      "Epoch 244/500\n",
      "514/514 [==============================] - 0s 119us/step - loss: 0.4013 - acc: 0.8054 - val_loss: 0.5136 - val_acc: 0.7362\n",
      "Epoch 245/500\n",
      "514/514 [==============================] - 0s 124us/step - loss: 0.4220 - acc: 0.7918 - val_loss: 0.5070 - val_acc: 0.7638\n",
      "Epoch 246/500\n",
      "514/514 [==============================] - 0s 115us/step - loss: 0.4131 - acc: 0.8035 - val_loss: 0.5258 - val_acc: 0.7323\n",
      "Epoch 247/500\n",
      "514/514 [==============================] - 0s 122us/step - loss: 0.4092 - acc: 0.8035 - val_loss: 0.5099 - val_acc: 0.7677\n",
      "Epoch 248/500\n",
      "514/514 [==============================] - 0s 133us/step - loss: 0.3999 - acc: 0.8074 - val_loss: 0.5291 - val_acc: 0.7362\n",
      "Epoch 249/500\n",
      "514/514 [==============================] - 0s 130us/step - loss: 0.3975 - acc: 0.8016 - val_loss: 0.5519 - val_acc: 0.7638\n",
      "Epoch 250/500\n",
      "514/514 [==============================] - 0s 151us/step - loss: 0.4068 - acc: 0.7918 - val_loss: 0.5531 - val_acc: 0.7244\n",
      "Epoch 251/500\n",
      "514/514 [==============================] - 0s 147us/step - loss: 0.4122 - acc: 0.7918 - val_loss: 0.5438 - val_acc: 0.7559\n",
      "Epoch 252/500\n",
      "514/514 [==============================] - 0s 132us/step - loss: 0.3955 - acc: 0.8093 - val_loss: 0.5417 - val_acc: 0.7283\n",
      "Epoch 253/500\n",
      "514/514 [==============================] - 0s 128us/step - loss: 0.3906 - acc: 0.8152 - val_loss: 0.5521 - val_acc: 0.7244\n",
      "Epoch 254/500\n",
      "514/514 [==============================] - 0s 136us/step - loss: 0.4151 - acc: 0.8113 - val_loss: 0.5530 - val_acc: 0.7677\n",
      "Epoch 255/500\n",
      "514/514 [==============================] - 0s 143us/step - loss: 0.4064 - acc: 0.8016 - val_loss: 0.5670 - val_acc: 0.7677\n",
      "Epoch 256/500\n",
      "514/514 [==============================] - 0s 149us/step - loss: 0.4062 - acc: 0.8016 - val_loss: 0.5610 - val_acc: 0.7362\n",
      "Epoch 257/500\n",
      "514/514 [==============================] - 0s 133us/step - loss: 0.4107 - acc: 0.7899 - val_loss: 0.5460 - val_acc: 0.7480\n",
      "Epoch 258/500\n",
      "514/514 [==============================] - 0s 129us/step - loss: 0.4022 - acc: 0.8113 - val_loss: 0.5419 - val_acc: 0.7638\n",
      "Epoch 259/500\n",
      "514/514 [==============================] - 0s 139us/step - loss: 0.4444 - acc: 0.7704 - val_loss: 0.5682 - val_acc: 0.7205\n",
      "Epoch 260/500\n",
      "514/514 [==============================] - 0s 145us/step - loss: 0.4088 - acc: 0.8171 - val_loss: 0.5482 - val_acc: 0.7717\n",
      "Epoch 261/500\n",
      "514/514 [==============================] - 0s 121us/step - loss: 0.4057 - acc: 0.7977 - val_loss: 0.5311 - val_acc: 0.7638\n",
      "Epoch 262/500\n",
      "514/514 [==============================] - 0s 123us/step - loss: 0.3988 - acc: 0.8074 - val_loss: 0.5361 - val_acc: 0.7717\n",
      "Epoch 263/500\n",
      "514/514 [==============================] - 0s 121us/step - loss: 0.4056 - acc: 0.8132 - val_loss: 0.5692 - val_acc: 0.7126\n",
      "Epoch 264/500\n",
      "514/514 [==============================] - 0s 115us/step - loss: 0.4083 - acc: 0.8016 - val_loss: 0.5311 - val_acc: 0.7520\n",
      "Epoch 265/500\n",
      "514/514 [==============================] - 0s 127us/step - loss: 0.3976 - acc: 0.8016 - val_loss: 0.5630 - val_acc: 0.7205\n",
      "Epoch 266/500\n",
      "514/514 [==============================] - 0s 120us/step - loss: 0.4242 - acc: 0.7743 - val_loss: 0.5567 - val_acc: 0.7559\n",
      "Epoch 267/500\n",
      "514/514 [==============================] - 0s 153us/step - loss: 0.4203 - acc: 0.7996 - val_loss: 0.5791 - val_acc: 0.7244\n",
      "Epoch 268/500\n",
      "514/514 [==============================] - 0s 135us/step - loss: 0.4052 - acc: 0.8016 - val_loss: 0.5334 - val_acc: 0.7717\n",
      "Epoch 269/500\n",
      "514/514 [==============================] - 0s 122us/step - loss: 0.4022 - acc: 0.8016 - val_loss: 0.5334 - val_acc: 0.7559\n",
      "Epoch 270/500\n",
      "514/514 [==============================] - 0s 132us/step - loss: 0.4123 - acc: 0.8113 - val_loss: 0.5147 - val_acc: 0.7677\n",
      "Epoch 271/500\n",
      "514/514 [==============================] - 0s 150us/step - loss: 0.4134 - acc: 0.8074 - val_loss: 0.5089 - val_acc: 0.7756\n",
      "Epoch 272/500\n",
      "514/514 [==============================] - 0s 125us/step - loss: 0.4003 - acc: 0.8093 - val_loss: 0.5077 - val_acc: 0.7441\n",
      "Epoch 273/500\n",
      "514/514 [==============================] - 0s 131us/step - loss: 0.4033 - acc: 0.8093 - val_loss: 0.5190 - val_acc: 0.7362\n",
      "Epoch 274/500\n",
      "514/514 [==============================] - 0s 134us/step - loss: 0.4079 - acc: 0.8016 - val_loss: 0.5224 - val_acc: 0.7795\n",
      "Epoch 275/500\n",
      "514/514 [==============================] - 0s 139us/step - loss: 0.4139 - acc: 0.8016 - val_loss: 0.5097 - val_acc: 0.7795\n",
      "Epoch 276/500\n",
      "514/514 [==============================] - 0s 124us/step - loss: 0.4982 - acc: 0.7626 - val_loss: 0.5471 - val_acc: 0.7323\n",
      "Epoch 277/500\n",
      "514/514 [==============================] - 0s 133us/step - loss: 0.4392 - acc: 0.7763 - val_loss: 0.5017 - val_acc: 0.7638\n",
      "Epoch 278/500\n",
      "514/514 [==============================] - 0s 122us/step - loss: 0.4259 - acc: 0.7957 - val_loss: 0.5142 - val_acc: 0.7402\n",
      "Epoch 279/500\n",
      "514/514 [==============================] - 0s 111us/step - loss: 0.4209 - acc: 0.8152 - val_loss: 0.5120 - val_acc: 0.7362\n",
      "Epoch 280/500\n",
      "514/514 [==============================] - 0s 112us/step - loss: 0.3995 - acc: 0.8054 - val_loss: 0.4987 - val_acc: 0.7638\n",
      "Epoch 281/500\n",
      "514/514 [==============================] - 0s 118us/step - loss: 0.4197 - acc: 0.7938 - val_loss: 0.5066 - val_acc: 0.7441\n",
      "Epoch 282/500\n",
      "514/514 [==============================] - 0s 122us/step - loss: 0.4187 - acc: 0.7840 - val_loss: 0.5388 - val_acc: 0.7087\n",
      "Epoch 283/500\n",
      "514/514 [==============================] - 0s 115us/step - loss: 0.4229 - acc: 0.8016 - val_loss: 0.5185 - val_acc: 0.7598\n",
      "Epoch 284/500\n",
      "514/514 [==============================] - 0s 139us/step - loss: 0.3991 - acc: 0.8113 - val_loss: 0.5076 - val_acc: 0.7441\n",
      "Epoch 285/500\n",
      "514/514 [==============================] - 0s 162us/step - loss: 0.3999 - acc: 0.8054 - val_loss: 0.5289 - val_acc: 0.7559\n",
      "Epoch 286/500\n",
      "514/514 [==============================] - 0s 125us/step - loss: 0.4032 - acc: 0.8152 - val_loss: 0.5488 - val_acc: 0.6969\n",
      "Epoch 287/500\n",
      "514/514 [==============================] - 0s 132us/step - loss: 0.4203 - acc: 0.8016 - val_loss: 0.5020 - val_acc: 0.7638\n",
      "Epoch 288/500\n",
      "514/514 [==============================] - 0s 128us/step - loss: 0.3965 - acc: 0.8113 - val_loss: 0.5188 - val_acc: 0.7283\n",
      "Epoch 289/500\n",
      "514/514 [==============================] - 0s 121us/step - loss: 0.3939 - acc: 0.8113 - val_loss: 0.5191 - val_acc: 0.7520\n",
      "Epoch 290/500\n",
      "514/514 [==============================] - 0s 113us/step - loss: 0.4057 - acc: 0.8074 - val_loss: 0.5228 - val_acc: 0.7283\n",
      "Epoch 291/500\n",
      "514/514 [==============================] - 0s 148us/step - loss: 0.4070 - acc: 0.8016 - val_loss: 0.5412 - val_acc: 0.6929\n",
      "Epoch 292/500\n",
      "514/514 [==============================] - 0s 123us/step - loss: 0.4127 - acc: 0.8093 - val_loss: 0.5089 - val_acc: 0.7362\n",
      "Epoch 293/500\n",
      "514/514 [==============================] - 0s 114us/step - loss: 0.3972 - acc: 0.8113 - val_loss: 0.5411 - val_acc: 0.7008\n",
      "Epoch 294/500\n",
      "514/514 [==============================] - 0s 114us/step - loss: 0.3934 - acc: 0.8113 - val_loss: 0.5029 - val_acc: 0.7559\n",
      "Epoch 295/500\n",
      "514/514 [==============================] - 0s 115us/step - loss: 0.3938 - acc: 0.8074 - val_loss: 0.5306 - val_acc: 0.7244\n",
      "Epoch 296/500\n",
      "514/514 [==============================] - 0s 114us/step - loss: 0.3943 - acc: 0.8230 - val_loss: 0.5076 - val_acc: 0.7913\n",
      "Epoch 297/500\n",
      "514/514 [==============================] - 0s 126us/step - loss: 0.4029 - acc: 0.8074 - val_loss: 0.5414 - val_acc: 0.7205\n",
      "Epoch 298/500\n",
      "514/514 [==============================] - 0s 136us/step - loss: 0.3998 - acc: 0.8035 - val_loss: 0.5123 - val_acc: 0.7362\n",
      "Epoch 299/500\n",
      "514/514 [==============================] - 0s 148us/step - loss: 0.4069 - acc: 0.8113 - val_loss: 0.5309 - val_acc: 0.7244\n",
      "Epoch 300/500\n",
      "514/514 [==============================] - 0s 130us/step - loss: 0.4379 - acc: 0.7840 - val_loss: 0.5149 - val_acc: 0.7795\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 301/500\n",
      "514/514 [==============================] - 0s 123us/step - loss: 0.4180 - acc: 0.8074 - val_loss: 0.5080 - val_acc: 0.7756\n",
      "Epoch 302/500\n",
      "514/514 [==============================] - 0s 121us/step - loss: 0.4361 - acc: 0.7918 - val_loss: 0.5217 - val_acc: 0.7323\n",
      "Epoch 303/500\n",
      "514/514 [==============================] - 0s 120us/step - loss: 0.4346 - acc: 0.7821 - val_loss: 0.4960 - val_acc: 0.7638\n",
      "Epoch 304/500\n",
      "514/514 [==============================] - 0s 118us/step - loss: 0.4211 - acc: 0.7899 - val_loss: 0.5088 - val_acc: 0.7480\n",
      "Epoch 305/500\n",
      "514/514 [==============================] - 0s 117us/step - loss: 0.4169 - acc: 0.7938 - val_loss: 0.5070 - val_acc: 0.7559\n",
      "Epoch 306/500\n",
      "514/514 [==============================] - 0s 121us/step - loss: 0.4040 - acc: 0.8016 - val_loss: 0.5292 - val_acc: 0.7323\n",
      "Epoch 307/500\n",
      "514/514 [==============================] - 0s 121us/step - loss: 0.3908 - acc: 0.8152 - val_loss: 0.5204 - val_acc: 0.7559\n",
      "Epoch 308/500\n",
      "514/514 [==============================] - 0s 120us/step - loss: 0.3956 - acc: 0.8152 - val_loss: 0.5181 - val_acc: 0.7362\n",
      "Epoch 309/500\n",
      "514/514 [==============================] - 0s 115us/step - loss: 0.3908 - acc: 0.8210 - val_loss: 0.5301 - val_acc: 0.7480\n",
      "Epoch 310/500\n",
      "514/514 [==============================] - 0s 144us/step - loss: 0.3937 - acc: 0.8113 - val_loss: 0.5190 - val_acc: 0.7362\n",
      "Epoch 311/500\n",
      "514/514 [==============================] - 0s 143us/step - loss: 0.4468 - acc: 0.7743 - val_loss: 0.5166 - val_acc: 0.7835\n",
      "Epoch 312/500\n",
      "514/514 [==============================] - 0s 132us/step - loss: 0.4109 - acc: 0.7996 - val_loss: 0.5097 - val_acc: 0.7756\n",
      "Epoch 313/500\n",
      "514/514 [==============================] - 0s 129us/step - loss: 0.4150 - acc: 0.8035 - val_loss: 0.5115 - val_acc: 0.7402\n",
      "Epoch 314/500\n",
      "514/514 [==============================] - 0s 128us/step - loss: 0.4040 - acc: 0.8074 - val_loss: 0.5071 - val_acc: 0.7520\n",
      "Epoch 315/500\n",
      "514/514 [==============================] - 0s 146us/step - loss: 0.4118 - acc: 0.7977 - val_loss: 0.5358 - val_acc: 0.7717\n",
      "Epoch 316/500\n",
      "514/514 [==============================] - 0s 128us/step - loss: 0.3999 - acc: 0.8171 - val_loss: 0.5190 - val_acc: 0.7402\n",
      "Epoch 317/500\n",
      "514/514 [==============================] - 0s 127us/step - loss: 0.3952 - acc: 0.8152 - val_loss: 0.5166 - val_acc: 0.7598\n",
      "Epoch 318/500\n",
      "514/514 [==============================] - 0s 128us/step - loss: 0.3903 - acc: 0.8074 - val_loss: 0.5237 - val_acc: 0.7441\n",
      "Epoch 319/500\n",
      "514/514 [==============================] - 0s 129us/step - loss: 0.4188 - acc: 0.8054 - val_loss: 0.5293 - val_acc: 0.7520\n",
      "Epoch 320/500\n",
      "514/514 [==============================] - 0s 133us/step - loss: 0.4059 - acc: 0.8054 - val_loss: 0.5173 - val_acc: 0.7520\n",
      "Epoch 321/500\n",
      "514/514 [==============================] - 0s 122us/step - loss: 0.3912 - acc: 0.8152 - val_loss: 0.5401 - val_acc: 0.7047\n",
      "Epoch 322/500\n",
      "514/514 [==============================] - 0s 129us/step - loss: 0.3968 - acc: 0.8093 - val_loss: 0.5415 - val_acc: 0.7756\n",
      "Epoch 323/500\n",
      "514/514 [==============================] - 0s 127us/step - loss: 0.3939 - acc: 0.8210 - val_loss: 0.5362 - val_acc: 0.7165\n",
      "Epoch 324/500\n",
      "514/514 [==============================] - 0s 139us/step - loss: 0.3921 - acc: 0.8210 - val_loss: 0.5283 - val_acc: 0.7717\n",
      "Epoch 325/500\n",
      "514/514 [==============================] - 0s 130us/step - loss: 0.3888 - acc: 0.8171 - val_loss: 0.5276 - val_acc: 0.7559\n",
      "Epoch 326/500\n",
      "514/514 [==============================] - 0s 116us/step - loss: 0.3922 - acc: 0.8035 - val_loss: 0.5254 - val_acc: 0.7638\n",
      "Epoch 327/500\n",
      "514/514 [==============================] - 0s 135us/step - loss: 0.4006 - acc: 0.8074 - val_loss: 0.5302 - val_acc: 0.7362\n",
      "Epoch 328/500\n",
      "514/514 [==============================] - 0s 134us/step - loss: 0.3825 - acc: 0.8132 - val_loss: 0.5348 - val_acc: 0.7362\n",
      "Epoch 329/500\n",
      "514/514 [==============================] - 0s 119us/step - loss: 0.3920 - acc: 0.8074 - val_loss: 0.5189 - val_acc: 0.7756\n",
      "Epoch 330/500\n",
      "514/514 [==============================] - 0s 150us/step - loss: 0.3939 - acc: 0.8113 - val_loss: 0.5498 - val_acc: 0.7244\n",
      "Epoch 331/500\n",
      "514/514 [==============================] - 0s 150us/step - loss: 0.4487 - acc: 0.7879 - val_loss: 0.5344 - val_acc: 0.7795\n",
      "Epoch 332/500\n",
      "514/514 [==============================] - 0s 129us/step - loss: 0.4249 - acc: 0.7977 - val_loss: 0.5239 - val_acc: 0.7323\n",
      "Epoch 333/500\n",
      "514/514 [==============================] - 0s 123us/step - loss: 0.3857 - acc: 0.8230 - val_loss: 0.5152 - val_acc: 0.7795\n",
      "Epoch 334/500\n",
      "514/514 [==============================] - 0s 134us/step - loss: 0.3851 - acc: 0.8191 - val_loss: 0.5220 - val_acc: 0.7677\n",
      "Epoch 335/500\n",
      "514/514 [==============================] - 0s 139us/step - loss: 0.3973 - acc: 0.8152 - val_loss: 0.5249 - val_acc: 0.7244\n",
      "Epoch 336/500\n",
      "514/514 [==============================] - 0s 149us/step - loss: 0.3814 - acc: 0.8191 - val_loss: 0.5270 - val_acc: 0.7835\n",
      "Epoch 337/500\n",
      "514/514 [==============================] - 0s 132us/step - loss: 0.3865 - acc: 0.8249 - val_loss: 0.5666 - val_acc: 0.7087\n",
      "Epoch 338/500\n",
      "514/514 [==============================] - 0s 133us/step - loss: 0.4297 - acc: 0.7860 - val_loss: 0.5330 - val_acc: 0.7638\n",
      "Epoch 339/500\n",
      "514/514 [==============================] - 0s 150us/step - loss: 0.4125 - acc: 0.8113 - val_loss: 0.5221 - val_acc: 0.7756\n",
      "Epoch 340/500\n",
      "514/514 [==============================] - 0s 142us/step - loss: 0.4066 - acc: 0.8016 - val_loss: 0.5748 - val_acc: 0.7047\n",
      "Epoch 341/500\n",
      "514/514 [==============================] - 0s 144us/step - loss: 0.4022 - acc: 0.8171 - val_loss: 0.5442 - val_acc: 0.7323\n",
      "Epoch 342/500\n",
      "514/514 [==============================] - 0s 132us/step - loss: 0.4236 - acc: 0.7879 - val_loss: 0.5076 - val_acc: 0.7874\n",
      "Epoch 343/500\n",
      "514/514 [==============================] - 0s 136us/step - loss: 0.4137 - acc: 0.7996 - val_loss: 0.5042 - val_acc: 0.7441\n",
      "Epoch 344/500\n",
      "514/514 [==============================] - 0s 142us/step - loss: 0.3961 - acc: 0.8113 - val_loss: 0.5440 - val_acc: 0.7441\n",
      "Epoch 345/500\n",
      "514/514 [==============================] - 0s 120us/step - loss: 0.3922 - acc: 0.8171 - val_loss: 0.5401 - val_acc: 0.7598\n",
      "Epoch 346/500\n",
      "514/514 [==============================] - 0s 155us/step - loss: 0.4287 - acc: 0.7957 - val_loss: 0.5130 - val_acc: 0.7638\n",
      "Epoch 347/500\n",
      "514/514 [==============================] - 0s 137us/step - loss: 0.4054 - acc: 0.7957 - val_loss: 0.5060 - val_acc: 0.7480\n",
      "Epoch 348/500\n",
      "514/514 [==============================] - 0s 132us/step - loss: 0.4022 - acc: 0.7957 - val_loss: 0.5053 - val_acc: 0.7598\n",
      "Epoch 349/500\n",
      "514/514 [==============================] - 0s 131us/step - loss: 0.3966 - acc: 0.8113 - val_loss: 0.5225 - val_acc: 0.7677\n",
      "Epoch 350/500\n",
      "514/514 [==============================] - 0s 141us/step - loss: 0.4051 - acc: 0.8191 - val_loss: 0.5251 - val_acc: 0.7362\n",
      "Epoch 351/500\n",
      "514/514 [==============================] - 0s 140us/step - loss: 0.4252 - acc: 0.7996 - val_loss: 0.5773 - val_acc: 0.7165\n",
      "Epoch 352/500\n",
      "514/514 [==============================] - 0s 131us/step - loss: 0.3911 - acc: 0.8074 - val_loss: 0.5314 - val_acc: 0.7756\n",
      "Epoch 353/500\n",
      "514/514 [==============================] - 0s 141us/step - loss: 0.3857 - acc: 0.8249 - val_loss: 0.5596 - val_acc: 0.7244\n",
      "Epoch 354/500\n",
      "514/514 [==============================] - 0s 136us/step - loss: 0.3896 - acc: 0.8249 - val_loss: 0.5475 - val_acc: 0.7244\n",
      "Epoch 355/500\n",
      "514/514 [==============================] - 0s 121us/step - loss: 0.3839 - acc: 0.8327 - val_loss: 0.5329 - val_acc: 0.7441\n",
      "Epoch 356/500\n",
      "514/514 [==============================] - 0s 122us/step - loss: 0.3885 - acc: 0.8249 - val_loss: 0.5316 - val_acc: 0.7756\n",
      "Epoch 357/500\n",
      "514/514 [==============================] - 0s 123us/step - loss: 0.3923 - acc: 0.8113 - val_loss: 0.5419 - val_acc: 0.7362\n",
      "Epoch 358/500\n",
      "514/514 [==============================] - 0s 119us/step - loss: 0.3834 - acc: 0.8210 - val_loss: 0.5381 - val_acc: 0.7323\n",
      "Epoch 359/500\n",
      "514/514 [==============================] - 0s 116us/step - loss: 0.3899 - acc: 0.8171 - val_loss: 0.5313 - val_acc: 0.7717\n",
      "Epoch 360/500\n",
      "514/514 [==============================] - 0s 121us/step - loss: 0.3907 - acc: 0.8249 - val_loss: 0.5575 - val_acc: 0.7323\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 361/500\n",
      "514/514 [==============================] - 0s 129us/step - loss: 0.4016 - acc: 0.8171 - val_loss: 0.5499 - val_acc: 0.7323\n",
      "Epoch 362/500\n",
      "514/514 [==============================] - 0s 113us/step - loss: 0.3770 - acc: 0.8249 - val_loss: 0.5276 - val_acc: 0.7638\n",
      "Epoch 363/500\n",
      "514/514 [==============================] - 0s 125us/step - loss: 0.3901 - acc: 0.8307 - val_loss: 0.5412 - val_acc: 0.7441\n",
      "Epoch 364/500\n",
      "514/514 [==============================] - 0s 118us/step - loss: 0.3830 - acc: 0.8307 - val_loss: 0.5350 - val_acc: 0.7441\n",
      "Epoch 365/500\n",
      "514/514 [==============================] - 0s 127us/step - loss: 0.3824 - acc: 0.8191 - val_loss: 0.5322 - val_acc: 0.7677\n",
      "Epoch 366/500\n",
      "514/514 [==============================] - 0s 127us/step - loss: 0.3791 - acc: 0.8191 - val_loss: 0.5499 - val_acc: 0.7323\n",
      "Epoch 367/500\n",
      "514/514 [==============================] - 0s 120us/step - loss: 0.3790 - acc: 0.8288 - val_loss: 0.5299 - val_acc: 0.7402\n",
      "Epoch 368/500\n",
      "514/514 [==============================] - 0s 122us/step - loss: 0.3777 - acc: 0.8307 - val_loss: 0.5428 - val_acc: 0.7480\n",
      "Epoch 369/500\n",
      "514/514 [==============================] - 0s 119us/step - loss: 0.3746 - acc: 0.8307 - val_loss: 0.5364 - val_acc: 0.7402\n",
      "Epoch 370/500\n",
      "514/514 [==============================] - 0s 123us/step - loss: 0.3739 - acc: 0.8288 - val_loss: 0.5488 - val_acc: 0.7559\n",
      "Epoch 371/500\n",
      "514/514 [==============================] - 0s 128us/step - loss: 0.3750 - acc: 0.8307 - val_loss: 0.5442 - val_acc: 0.7362\n",
      "Epoch 372/500\n",
      "514/514 [==============================] - 0s 129us/step - loss: 0.3846 - acc: 0.8230 - val_loss: 0.5650 - val_acc: 0.7244\n",
      "Epoch 373/500\n",
      "514/514 [==============================] - 0s 130us/step - loss: 0.3749 - acc: 0.8366 - val_loss: 0.5373 - val_acc: 0.7480\n",
      "Epoch 374/500\n",
      "514/514 [==============================] - 0s 136us/step - loss: 0.3745 - acc: 0.8230 - val_loss: 0.5709 - val_acc: 0.7362\n",
      "Epoch 375/500\n",
      "514/514 [==============================] - 0s 133us/step - loss: 0.4018 - acc: 0.8016 - val_loss: 0.5645 - val_acc: 0.7047\n",
      "Epoch 376/500\n",
      "514/514 [==============================] - 0s 125us/step - loss: 0.3909 - acc: 0.8152 - val_loss: 0.5502 - val_acc: 0.7598\n",
      "Epoch 377/500\n",
      "514/514 [==============================] - 0s 130us/step - loss: 0.3757 - acc: 0.8288 - val_loss: 0.5416 - val_acc: 0.7520\n",
      "Epoch 378/500\n",
      "514/514 [==============================] - 0s 126us/step - loss: 0.3845 - acc: 0.8249 - val_loss: 0.5433 - val_acc: 0.7441\n",
      "Epoch 379/500\n",
      "514/514 [==============================] - 0s 131us/step - loss: 0.3794 - acc: 0.8230 - val_loss: 0.5571 - val_acc: 0.7402\n",
      "Epoch 380/500\n",
      "514/514 [==============================] - 0s 124us/step - loss: 0.4238 - acc: 0.7996 - val_loss: 0.5420 - val_acc: 0.7559\n",
      "Epoch 381/500\n",
      "514/514 [==============================] - 0s 132us/step - loss: 0.3884 - acc: 0.8230 - val_loss: 0.5469 - val_acc: 0.7756\n",
      "Epoch 382/500\n",
      "514/514 [==============================] - 0s 125us/step - loss: 0.4689 - acc: 0.7918 - val_loss: 0.5524 - val_acc: 0.7283\n",
      "Epoch 383/500\n",
      "514/514 [==============================] - 0s 132us/step - loss: 0.4172 - acc: 0.7996 - val_loss: 0.5177 - val_acc: 0.7717\n",
      "Epoch 384/500\n",
      "514/514 [==============================] - 0s 131us/step - loss: 0.4023 - acc: 0.8093 - val_loss: 0.5254 - val_acc: 0.7520\n",
      "Epoch 385/500\n",
      "514/514 [==============================] - 0s 138us/step - loss: 0.4163 - acc: 0.8016 - val_loss: 0.5190 - val_acc: 0.7598\n",
      "Epoch 386/500\n",
      "514/514 [==============================] - 0s 131us/step - loss: 0.3914 - acc: 0.8113 - val_loss: 0.5136 - val_acc: 0.7598\n",
      "Epoch 387/500\n",
      "514/514 [==============================] - 0s 141us/step - loss: 0.3942 - acc: 0.8054 - val_loss: 0.5276 - val_acc: 0.7480\n",
      "Epoch 388/500\n",
      "514/514 [==============================] - 0s 141us/step - loss: 0.3994 - acc: 0.8093 - val_loss: 0.5308 - val_acc: 0.7441\n",
      "Epoch 389/500\n",
      "514/514 [==============================] - 0s 151us/step - loss: 0.4096 - acc: 0.8113 - val_loss: 0.5204 - val_acc: 0.7520\n",
      "Epoch 390/500\n",
      "514/514 [==============================] - 0s 139us/step - loss: 0.4085 - acc: 0.7957 - val_loss: 0.5229 - val_acc: 0.7402\n",
      "Epoch 391/500\n",
      "514/514 [==============================] - 0s 140us/step - loss: 0.4071 - acc: 0.7977 - val_loss: 0.5241 - val_acc: 0.7520\n",
      "Epoch 392/500\n",
      "514/514 [==============================] - 0s 118us/step - loss: 0.3973 - acc: 0.8132 - val_loss: 0.5223 - val_acc: 0.7362\n",
      "Epoch 393/500\n",
      "514/514 [==============================] - 0s 132us/step - loss: 0.3896 - acc: 0.8113 - val_loss: 0.5190 - val_acc: 0.7402\n",
      "Epoch 394/500\n",
      "514/514 [==============================] - 0s 139us/step - loss: 0.3926 - acc: 0.8210 - val_loss: 0.5505 - val_acc: 0.7244\n",
      "Epoch 395/500\n",
      "514/514 [==============================] - 0s 137us/step - loss: 0.4073 - acc: 0.8035 - val_loss: 0.5166 - val_acc: 0.7638\n",
      "Epoch 396/500\n",
      "514/514 [==============================] - 0s 134us/step - loss: 0.3966 - acc: 0.8093 - val_loss: 0.5197 - val_acc: 0.7638\n",
      "Epoch 397/500\n",
      "514/514 [==============================] - 0s 121us/step - loss: 0.3960 - acc: 0.8249 - val_loss: 0.5660 - val_acc: 0.7205\n",
      "Epoch 398/500\n",
      "514/514 [==============================] - 0s 115us/step - loss: 0.4019 - acc: 0.8054 - val_loss: 0.5238 - val_acc: 0.7559\n",
      "Epoch 399/500\n",
      "514/514 [==============================] - 0s 138us/step - loss: 0.4047 - acc: 0.8113 - val_loss: 0.5253 - val_acc: 0.7638\n",
      "Epoch 400/500\n",
      "514/514 [==============================] - 0s 120us/step - loss: 0.3878 - acc: 0.8249 - val_loss: 0.5321 - val_acc: 0.7362\n",
      "Epoch 401/500\n",
      "514/514 [==============================] - 0s 120us/step - loss: 0.3910 - acc: 0.8132 - val_loss: 0.5396 - val_acc: 0.7323\n",
      "Epoch 402/500\n",
      "514/514 [==============================] - 0s 115us/step - loss: 0.4103 - acc: 0.8171 - val_loss: 0.5151 - val_acc: 0.7559\n",
      "Epoch 403/500\n",
      "514/514 [==============================] - 0s 119us/step - loss: 0.4041 - acc: 0.8152 - val_loss: 0.5272 - val_acc: 0.7480\n",
      "Epoch 404/500\n",
      "514/514 [==============================] - 0s 135us/step - loss: 0.3992 - acc: 0.8152 - val_loss: 0.5750 - val_acc: 0.7283\n",
      "Epoch 405/500\n",
      "514/514 [==============================] - 0s 152us/step - loss: 0.4236 - acc: 0.7977 - val_loss: 0.5287 - val_acc: 0.7598\n",
      "Epoch 406/500\n",
      "514/514 [==============================] - 0s 121us/step - loss: 0.3905 - acc: 0.8132 - val_loss: 0.5378 - val_acc: 0.7441\n",
      "Epoch 407/500\n",
      "514/514 [==============================] - 0s 117us/step - loss: 0.3894 - acc: 0.8249 - val_loss: 0.5321 - val_acc: 0.7244\n",
      "Epoch 408/500\n",
      "514/514 [==============================] - 0s 138us/step - loss: 0.3883 - acc: 0.8230 - val_loss: 0.5407 - val_acc: 0.7323\n",
      "Epoch 409/500\n",
      "514/514 [==============================] - 0s 142us/step - loss: 0.4133 - acc: 0.8074 - val_loss: 0.5317 - val_acc: 0.7795\n",
      "Epoch 410/500\n",
      "514/514 [==============================] - 0s 123us/step - loss: 0.3939 - acc: 0.8191 - val_loss: 0.5245 - val_acc: 0.7362\n",
      "Epoch 411/500\n",
      "514/514 [==============================] - 0s 121us/step - loss: 0.4022 - acc: 0.8113 - val_loss: 0.5135 - val_acc: 0.7362\n",
      "Epoch 412/500\n",
      "514/514 [==============================] - 0s 132us/step - loss: 0.3923 - acc: 0.8132 - val_loss: 0.5214 - val_acc: 0.7717\n",
      "Epoch 413/500\n",
      "514/514 [==============================] - 0s 142us/step - loss: 0.4073 - acc: 0.8268 - val_loss: 0.5240 - val_acc: 0.7205\n",
      "Epoch 414/500\n",
      "514/514 [==============================] - 0s 158us/step - loss: 0.3984 - acc: 0.8093 - val_loss: 0.5233 - val_acc: 0.7598\n",
      "Epoch 415/500\n",
      "514/514 [==============================] - 0s 148us/step - loss: 0.4047 - acc: 0.8249 - val_loss: 0.5238 - val_acc: 0.7323\n",
      "Epoch 416/500\n",
      "514/514 [==============================] - 0s 138us/step - loss: 0.3956 - acc: 0.8152 - val_loss: 0.5368 - val_acc: 0.7205\n",
      "Epoch 417/500\n",
      "514/514 [==============================] - 0s 149us/step - loss: 0.3921 - acc: 0.7977 - val_loss: 0.5079 - val_acc: 0.7441\n",
      "Epoch 418/500\n",
      "514/514 [==============================] - 0s 146us/step - loss: 0.3811 - acc: 0.8132 - val_loss: 0.5199 - val_acc: 0.7441\n",
      "Epoch 419/500\n",
      "514/514 [==============================] - 0s 142us/step - loss: 0.3940 - acc: 0.8093 - val_loss: 0.5164 - val_acc: 0.7638\n",
      "Epoch 420/500\n",
      "514/514 [==============================] - 0s 147us/step - loss: 0.3973 - acc: 0.8171 - val_loss: 0.5213 - val_acc: 0.7520\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 421/500\n",
      "514/514 [==============================] - 0s 157us/step - loss: 0.3918 - acc: 0.8074 - val_loss: 0.5524 - val_acc: 0.7047\n",
      "Epoch 422/500\n",
      "514/514 [==============================] - 0s 153us/step - loss: 0.4523 - acc: 0.7802 - val_loss: 0.5314 - val_acc: 0.7717\n",
      "Epoch 423/500\n",
      "514/514 [==============================] - 0s 153us/step - loss: 0.4117 - acc: 0.8016 - val_loss: 0.5344 - val_acc: 0.7323\n",
      "Epoch 424/500\n",
      "514/514 [==============================] - 0s 120us/step - loss: 0.3994 - acc: 0.8054 - val_loss: 0.5205 - val_acc: 0.7323\n",
      "Epoch 425/500\n",
      "514/514 [==============================] - 0s 129us/step - loss: 0.3930 - acc: 0.8016 - val_loss: 0.5160 - val_acc: 0.7835\n",
      "Epoch 426/500\n",
      "514/514 [==============================] - 0s 121us/step - loss: 0.3980 - acc: 0.8132 - val_loss: 0.5117 - val_acc: 0.7520\n",
      "Epoch 427/500\n",
      "514/514 [==============================] - 0s 122us/step - loss: 0.4045 - acc: 0.7996 - val_loss: 0.5258 - val_acc: 0.7402\n",
      "Epoch 428/500\n",
      "514/514 [==============================] - 0s 117us/step - loss: 0.3987 - acc: 0.8307 - val_loss: 0.5312 - val_acc: 0.7205\n",
      "Epoch 429/500\n",
      "514/514 [==============================] - 0s 148us/step - loss: 0.3907 - acc: 0.8210 - val_loss: 0.5141 - val_acc: 0.7441\n",
      "Epoch 430/500\n",
      "514/514 [==============================] - 0s 136us/step - loss: 0.4095 - acc: 0.7996 - val_loss: 0.5340 - val_acc: 0.7795\n",
      "Epoch 431/500\n",
      "514/514 [==============================] - 0s 129us/step - loss: 0.3910 - acc: 0.8268 - val_loss: 0.5529 - val_acc: 0.7087\n",
      "Epoch 432/500\n",
      "514/514 [==============================] - 0s 150us/step - loss: 0.4053 - acc: 0.8035 - val_loss: 0.5195 - val_acc: 0.7323\n",
      "Epoch 433/500\n",
      "514/514 [==============================] - 0s 140us/step - loss: 0.3876 - acc: 0.8268 - val_loss: 0.5181 - val_acc: 0.7244\n",
      "Epoch 434/500\n",
      "514/514 [==============================] - 0s 138us/step - loss: 0.3861 - acc: 0.8210 - val_loss: 0.5235 - val_acc: 0.7402\n",
      "Epoch 435/500\n",
      "514/514 [==============================] - 0s 136us/step - loss: 0.4517 - acc: 0.7918 - val_loss: 0.5132 - val_acc: 0.7402\n",
      "Epoch 436/500\n",
      "514/514 [==============================] - 0s 132us/step - loss: 0.4397 - acc: 0.7763 - val_loss: 0.5174 - val_acc: 0.8031\n",
      "Epoch 437/500\n",
      "514/514 [==============================] - 0s 122us/step - loss: 0.4254 - acc: 0.7918 - val_loss: 0.5264 - val_acc: 0.7362\n",
      "Epoch 438/500\n",
      "514/514 [==============================] - 0s 129us/step - loss: 0.4086 - acc: 0.8035 - val_loss: 0.5106 - val_acc: 0.7638\n",
      "Epoch 439/500\n",
      "514/514 [==============================] - 0s 126us/step - loss: 0.4185 - acc: 0.7938 - val_loss: 0.5098 - val_acc: 0.7795\n",
      "Epoch 440/500\n",
      "514/514 [==============================] - 0s 131us/step - loss: 0.4272 - acc: 0.7802 - val_loss: 0.5188 - val_acc: 0.7441\n",
      "Epoch 441/500\n",
      "514/514 [==============================] - 0s 123us/step - loss: 0.4082 - acc: 0.8054 - val_loss: 0.5125 - val_acc: 0.7677\n",
      "Epoch 442/500\n",
      "514/514 [==============================] - 0s 131us/step - loss: 0.4024 - acc: 0.8054 - val_loss: 0.5082 - val_acc: 0.7677\n",
      "Epoch 443/500\n",
      "514/514 [==============================] - 0s 135us/step - loss: 0.4068 - acc: 0.8074 - val_loss: 0.5082 - val_acc: 0.7677\n",
      "Epoch 444/500\n",
      "514/514 [==============================] - 0s 124us/step - loss: 0.4169 - acc: 0.8016 - val_loss: 0.5168 - val_acc: 0.7913\n",
      "Epoch 445/500\n",
      "514/514 [==============================] - 0s 129us/step - loss: 0.4144 - acc: 0.7957 - val_loss: 0.5168 - val_acc: 0.7598\n",
      "Epoch 446/500\n",
      "514/514 [==============================] - 0s 128us/step - loss: 0.4115 - acc: 0.7957 - val_loss: 0.5160 - val_acc: 0.7638\n",
      "Epoch 447/500\n",
      "514/514 [==============================] - 0s 151us/step - loss: 0.4195 - acc: 0.7996 - val_loss: 0.5207 - val_acc: 0.7874\n",
      "Epoch 448/500\n",
      "514/514 [==============================] - 0s 134us/step - loss: 0.4079 - acc: 0.7938 - val_loss: 0.5152 - val_acc: 0.7677\n",
      "Epoch 449/500\n",
      "514/514 [==============================] - 0s 128us/step - loss: 0.4039 - acc: 0.8074 - val_loss: 0.5363 - val_acc: 0.7598\n",
      "Epoch 450/500\n",
      "514/514 [==============================] - 0s 133us/step - loss: 0.4013 - acc: 0.8074 - val_loss: 0.5385 - val_acc: 0.7913\n",
      "Epoch 451/500\n",
      "514/514 [==============================] - 0s 136us/step - loss: 0.4162 - acc: 0.7918 - val_loss: 0.5276 - val_acc: 0.7480\n",
      "Epoch 452/500\n",
      "514/514 [==============================] - 0s 146us/step - loss: 0.3971 - acc: 0.7977 - val_loss: 0.5329 - val_acc: 0.7913\n",
      "Epoch 453/500\n",
      "514/514 [==============================] - 0s 136us/step - loss: 0.4036 - acc: 0.8191 - val_loss: 0.5452 - val_acc: 0.7441\n",
      "Epoch 454/500\n",
      "514/514 [==============================] - 0s 121us/step - loss: 0.4103 - acc: 0.7996 - val_loss: 0.5253 - val_acc: 0.7717\n",
      "Epoch 455/500\n",
      "514/514 [==============================] - 0s 142us/step - loss: 0.4026 - acc: 0.8074 - val_loss: 0.5223 - val_acc: 0.7835\n",
      "Epoch 456/500\n",
      "514/514 [==============================] - 0s 121us/step - loss: 0.4201 - acc: 0.7957 - val_loss: 0.5455 - val_acc: 0.7362\n",
      "Epoch 457/500\n",
      "514/514 [==============================] - 0s 128us/step - loss: 0.4187 - acc: 0.8093 - val_loss: 0.5331 - val_acc: 0.7953\n",
      "Epoch 458/500\n",
      "514/514 [==============================] - 0s 133us/step - loss: 0.4047 - acc: 0.8152 - val_loss: 0.5134 - val_acc: 0.7480\n",
      "Epoch 459/500\n",
      "514/514 [==============================] - 0s 130us/step - loss: 0.3975 - acc: 0.8113 - val_loss: 0.5236 - val_acc: 0.7480\n",
      "Epoch 460/500\n",
      "514/514 [==============================] - 0s 133us/step - loss: 0.3934 - acc: 0.8171 - val_loss: 0.5169 - val_acc: 0.7559\n",
      "Epoch 461/500\n",
      "514/514 [==============================] - 0s 163us/step - loss: 0.4030 - acc: 0.8054 - val_loss: 0.5201 - val_acc: 0.7480\n",
      "Epoch 462/500\n",
      "514/514 [==============================] - 0s 169us/step - loss: 0.3915 - acc: 0.8191 - val_loss: 0.5214 - val_acc: 0.7598\n",
      "Epoch 463/500\n",
      "514/514 [==============================] - 0s 145us/step - loss: 0.3932 - acc: 0.8132 - val_loss: 0.5219 - val_acc: 0.7480\n",
      "Epoch 464/500\n",
      "514/514 [==============================] - 0s 144us/step - loss: 0.3898 - acc: 0.8113 - val_loss: 0.5221 - val_acc: 0.7480\n",
      "Epoch 465/500\n",
      "514/514 [==============================] - 0s 142us/step - loss: 0.3962 - acc: 0.8249 - val_loss: 0.5349 - val_acc: 0.7913\n",
      "Epoch 466/500\n",
      "514/514 [==============================] - 0s 139us/step - loss: 0.4005 - acc: 0.8132 - val_loss: 0.5220 - val_acc: 0.7362\n",
      "Epoch 467/500\n",
      "514/514 [==============================] - 0s 144us/step - loss: 0.3881 - acc: 0.8191 - val_loss: 0.5168 - val_acc: 0.7441\n",
      "Epoch 468/500\n",
      "514/514 [==============================] - 0s 149us/step - loss: 0.3849 - acc: 0.8152 - val_loss: 0.5166 - val_acc: 0.7677\n",
      "Epoch 469/500\n",
      "514/514 [==============================] - 0s 141us/step - loss: 0.3840 - acc: 0.8171 - val_loss: 0.5283 - val_acc: 0.7283\n",
      "Epoch 470/500\n",
      "514/514 [==============================] - 0s 148us/step - loss: 0.3837 - acc: 0.8230 - val_loss: 0.5259 - val_acc: 0.7362\n",
      "Epoch 471/500\n",
      "514/514 [==============================] - 0s 147us/step - loss: 0.3864 - acc: 0.8191 - val_loss: 0.5357 - val_acc: 0.7205\n",
      "Epoch 472/500\n",
      "514/514 [==============================] - 0s 150us/step - loss: 0.4094 - acc: 0.7977 - val_loss: 0.5170 - val_acc: 0.7677\n",
      "Epoch 473/500\n",
      "514/514 [==============================] - 0s 137us/step - loss: 0.4147 - acc: 0.8210 - val_loss: 0.5264 - val_acc: 0.7047\n",
      "Epoch 474/500\n",
      "514/514 [==============================] - 0s 155us/step - loss: 0.3941 - acc: 0.8249 - val_loss: 0.5190 - val_acc: 0.7756\n",
      "Epoch 475/500\n",
      "514/514 [==============================] - 0s 125us/step - loss: 0.3884 - acc: 0.8249 - val_loss: 0.5392 - val_acc: 0.7283\n",
      "Epoch 476/500\n",
      "514/514 [==============================] - 0s 146us/step - loss: 0.3842 - acc: 0.8191 - val_loss: 0.5254 - val_acc: 0.7756\n",
      "Epoch 477/500\n",
      "514/514 [==============================] - 0s 140us/step - loss: 0.4016 - acc: 0.8113 - val_loss: 0.5221 - val_acc: 0.7244\n",
      "Epoch 478/500\n",
      "514/514 [==============================] - 0s 137us/step - loss: 0.4206 - acc: 0.8152 - val_loss: 0.5520 - val_acc: 0.7323\n",
      "Epoch 479/500\n",
      "514/514 [==============================] - 0s 134us/step - loss: 0.4295 - acc: 0.7899 - val_loss: 0.5172 - val_acc: 0.7638\n",
      "Epoch 480/500\n",
      "514/514 [==============================] - 0s 137us/step - loss: 0.4049 - acc: 0.8113 - val_loss: 0.5144 - val_acc: 0.7559\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 481/500\n",
      "514/514 [==============================] - 0s 146us/step - loss: 0.4036 - acc: 0.8113 - val_loss: 0.5242 - val_acc: 0.7323\n",
      "Epoch 482/500\n",
      "514/514 [==============================] - 0s 132us/step - loss: 0.3909 - acc: 0.8171 - val_loss: 0.5355 - val_acc: 0.7402\n",
      "Epoch 483/500\n",
      "514/514 [==============================] - 0s 131us/step - loss: 0.3843 - acc: 0.8191 - val_loss: 0.5292 - val_acc: 0.7323\n",
      "Epoch 484/500\n",
      "514/514 [==============================] - 0s 134us/step - loss: 0.3826 - acc: 0.8268 - val_loss: 0.5282 - val_acc: 0.7677\n",
      "Epoch 485/500\n",
      "514/514 [==============================] - 0s 130us/step - loss: 0.4216 - acc: 0.8074 - val_loss: 0.5650 - val_acc: 0.7126\n",
      "Epoch 486/500\n",
      "514/514 [==============================] - 0s 142us/step - loss: 0.3974 - acc: 0.8035 - val_loss: 0.5169 - val_acc: 0.7795\n",
      "Epoch 487/500\n",
      "514/514 [==============================] - 0s 135us/step - loss: 0.3899 - acc: 0.8171 - val_loss: 0.5348 - val_acc: 0.7402\n",
      "Epoch 488/500\n",
      "514/514 [==============================] - 0s 130us/step - loss: 0.4348 - acc: 0.7957 - val_loss: 0.5200 - val_acc: 0.7913\n",
      "Epoch 489/500\n",
      "514/514 [==============================] - 0s 119us/step - loss: 0.3936 - acc: 0.8152 - val_loss: 0.5306 - val_acc: 0.7205\n",
      "Epoch 490/500\n",
      "514/514 [==============================] - 0s 124us/step - loss: 0.4030 - acc: 0.7957 - val_loss: 0.5225 - val_acc: 0.7638\n",
      "Epoch 491/500\n",
      "514/514 [==============================] - 0s 141us/step - loss: 0.3868 - acc: 0.8191 - val_loss: 0.5175 - val_acc: 0.7362\n",
      "Epoch 492/500\n",
      "514/514 [==============================] - 0s 146us/step - loss: 0.3856 - acc: 0.8210 - val_loss: 0.5182 - val_acc: 0.7835\n",
      "Epoch 493/500\n",
      "514/514 [==============================] - 0s 131us/step - loss: 0.3873 - acc: 0.8113 - val_loss: 0.5237 - val_acc: 0.7559\n",
      "Epoch 494/500\n",
      "514/514 [==============================] - 0s 122us/step - loss: 0.4361 - acc: 0.7860 - val_loss: 0.5472 - val_acc: 0.7441\n",
      "Epoch 495/500\n",
      "514/514 [==============================] - 0s 159us/step - loss: 0.3898 - acc: 0.8132 - val_loss: 0.5191 - val_acc: 0.7638\n",
      "Epoch 496/500\n",
      "514/514 [==============================] - 0s 149us/step - loss: 0.4003 - acc: 0.8152 - val_loss: 0.5199 - val_acc: 0.7520\n",
      "Epoch 497/500\n",
      "514/514 [==============================] - 0s 134us/step - loss: 0.3851 - acc: 0.8249 - val_loss: 0.5209 - val_acc: 0.7520\n",
      "Epoch 498/500\n",
      "514/514 [==============================] - 0s 148us/step - loss: 0.3957 - acc: 0.8171 - val_loss: 0.5142 - val_acc: 0.7559\n",
      "Epoch 499/500\n",
      "514/514 [==============================] - 0s 145us/step - loss: 0.3845 - acc: 0.8230 - val_loss: 0.5271 - val_acc: 0.7402\n",
      "Epoch 500/500\n",
      "514/514 [==============================] - 0s 119us/step - loss: 0.4000 - acc: 0.8171 - val_loss: 0.5325 - val_acc: 0.7795\n"
     ]
    }
   ],
   "source": [
    "H = model.fit(X_train, y_binary_train, validation_data=(X_test, y_binary_test),epochs = 500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['val_loss', 'val_acc', 'loss', 'acc'])"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "H.history.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1a435605c0>]"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(H.history[\"loss\"])\n",
    "plt.plot(H.history[\"val_loss\"], 'r')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1a43659550>]"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(H.history[\"acc\"])\n",
    "plt.plot(H.history[\"val_acc\"], 'r')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Predict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(254,)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_softmax = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = np.argmax(y_pred_softmax, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python (spinningup)",
   "language": "python",
   "name": "spinningup"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
